bluuebunny/crossref_metadata_2025 huggingface dataset:
Using a simple parameterization for representing the effect of heterogeneous urban canopies in atmospheric models
Authors: Cheng Ge , Schlünzen K. Heinke
Published: July 2024

Increasing urbanization requires a better understanding and representation of urban effects and urban canopy processes in weather and climate modelling at various scales. In this work, an urban canopy parameterization (UCP) based on an extended nudging approach is presented. The UCP is implemented in the mesoscale model METRAS with a horizontal resolution of 500 m. The city of Hamburg with heterogeneous surfaces was chosen as the study area. Urban canopy information for Hamburg, including building height and building surface fraction (i.e., the ratio of the surface area occupied by buildings to the total plan area), obtained from the 3D city model of Hamburg LoD1 (Level of Detail 1). were used as input for the UCP.Model results show that the urban canopy parameterization based on an extended nudging approach can reproduce urban effects on the wind and temperature fields. For example, the UCP can simulate the wind reduction effects due to canopy obstacles in different model levels, with the largest wind reduction simulated in the urban areas with highest values for building surface fraction (densely built areas). In addition, the urban heat island effect as simulated with the single layer flux aggregation scheme in METRAS, was enhanced using the urban canopy parameterization, with the highest intensities in the densely built areas. The results suggest that nudging is a useful tool for modeling aerodynamic and thermodynamic urban effects, which are both important components in understanding the urban environment. Additionally, nudging is an approach that is relatively easy to implement or already implemented in many models. It can be applied to a variety of urban scenarios and resolutions and thus can be a simple approach to better represent urban effects in global-scale weather and climate models.
Recent meteorological tsunamis and other anomalous tidal surge events in western Europe
Authors: Renzi Emiliano , Bergin Claire , Kokina Tatjana , Pelaez-Zapata Daniel S. , Giles Daniel , Dias Frederic
Published: July 2024

We explore instances of unusual long-wave activity of meteorological origin along the coastal waters of north-west Europe during the summer of 2022. These events include meteorological tsunamis, i.e. waves in the tsunami frequency band originated by sharp atmospheric pressure variations and amplified by multiple resonant effects, and wind-generated infragravity waves which can trigger local seiches in enclosed basins and harboursAnomalous "tidal surges" were observed on 18 June 2022 in Wales, followed by similar occurrences in Ireland, France, and Spain. Additionally, several anomalous long-wave events were reported in south England and Wales on the morning of 19 July 2022. Our investigation involved analysing surface and high-altitude air pressure fields, as well as sea level oscillations for both days.We determine that the events on 18 June were a series of meteorological tsunamis, spreading across several western European countries and initiated by localised pressure disturbances originating within a low-pressure system over the North Atlantic Ocean. A local examination of the southern coast of Ireland suggests that Proudman resonance played a key role in amplifying the meteotsunami as it travelled eastward in the afternoon of 18 June. Similarly, our analysis of the events on 19 July indicates that the tidal surge observed in the UK and anomalous signals recorded in Ireland and France were likely instances of seiching triggered by infragravity waves. We conducted numerical simulations of the 18 June event using Volna-OP2, which solves the non-linear shallow water equations employing a finite volume discretisation technique. We also examined the influence of atmospheric wave velocity on the amplification of sea surface elevation.
Glacier ice loss in Central-Western Greenland from last-millennium maximum to present
Authors: Bonsoms Josep , Oliva Marc , López-Moreno Juan Ignacio , Jouvet Guillaume
Published: July 2024

The melting of snow and ice in Greenland has accelerated since the 1990s, resulting in significant impacts on the ecosystem, such as sea-level rise. However, the spatiotemporal evolution of Greenland's peripheral glaciers and ice caps (GICs) during the late Holocene period remains poorly understood. Understanding the maximum extent of these glaciers during the last millennium contextualize ongoing glacier trends in response to a changing climate. Currently, there is a lack of agreement between geological evidence of last-millennium maximum moraine extension and the prevailing climate conditions of that time. In this study, we aimed to model the past evolution of GICs in Central-Western Greenland and estimate anomalies relative to present-day conditions to reconcile past climate and quantify committed ice loss within a changing climate. We utilized the Instructed Glacier Model (IGM), a physically based glacier model incorporating mass conservation principles and deep learning emulator to estimate 3D ice-flow dynamics. Initially, the IGM was calibrated and validated using an ensemble of model parameterization options and climate perturbed conditions to reproduce the actual glacier area and ice thickness. The model successfully replicated ice-thickness and glaciers extension, reaching stable-state conditions for glacier area after a 500-year model run. This validated model was then forced with a range of potential air temperature and precipitation conditions based on estimates derived from ice core data near the study area. The resulting maximum glacier termini were validated with cosmogenic dating evidence from the last millennium (late Medieval Warm Period and onset of the Little Ice Age). The results indicated that air temperatures were at least < -0.75 ºC compared to the baseline climate (1960-1990 period) or 10%. These calibrated climate conditions suggested a reduction in glacier area and an ice thickness of approximately 20% compared to near present-day (2022) conditions. Using positive degree-day function calibrated with mass balance data, and a long-term model run (500 years after calibration), an increase of +1ºC would lead to a decrease in glacier area and ice thickness of around >50% compared to present-day conditions. These findings provide insights into the past glacier evolution within a long-term temporal perspective and help contextualize ongoing glacier retreat in response to climate change.
Retrieving thermodynamic profiles in the atmospheric boundary layer from ground-based passive remote sensing instruments using an optimal estimation physical retrieval 
Authors: Adler Bianca , Turner David D. , Bianco Laura , Wilczak James , Djalalova Irina , Myers Timothy
Published: July 2024

Thermodynamic profiles in the atmospheric boundary layer can be retrieved from ground-based passive remote sensing instruments with an optimal estimation physical retrieval such as Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe). The retrieval combines measurements, prior information, and corresponding uncertainties to find an optimal solution of the atmospheric state. TROPoe permits combining passively sensed radiances from infrared spectrometers and microwave radiometers with thermodynamic profiles from Raman lidars, Differential Absorption lidars, Radio Acoustic Sounding Systems, radio soundings, or numerical weather prediction models. After more than 10 years of development, the TROPoe retrieval code was recently converted to Python and put into a Docker container to facilitate its usage for both operations and research. It is currently used operationally by the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program and by the Swiss weather service MeteoSwiss as part of the EUMETNET. With a high temporal resolution on the order of minutes, the retrieved thermodynamic profiles are a powerful tool to study the temporal evolution of the boundary layer.Since each profile is retrieved independently from the previous one, the time series of thermodynamic variables contain random uncorrelated noise, which may hinder the study of diurnal cycles and temporal tendencies. In this work, we investigate how the temporal consistency of thermodynamic profiles retrieved with TROPoe can be improved by including information from a previous retrieved profile as input to the retrieval. We demonstrate that this method works well in mid-latitudes, polar and tropical sites and for retrievals based on measurements from infrared spectrometers and microwave radiometers. We further present methods to enhance the availability of valid profiles retrieved from infrared spectrometers by preventing overfitting and by adding information from an additional infrared band in high moisture environments when the typically used spectral bands are saturated.
Spatially sliding classifications of atmospheric circulation patterns: a tool to explain skewness of day-to-day temperature differences
Authors: Huth Radan , Stryhal Jan , Sedlák Pavel , Krauskopf Tomáš
Published: July 2024

Classifications of circulation patterns have been widely used in synoptic climatology for decades. Recently, this concept has been extended towards spatially sliding classifications, i.e., large sets (arrays) of classifications conducted independently for individual grid points.We make use of spatially sliding classifications in an attempt to explain asymmetry of statistical distributions of day-to-day temperature difference (DTD). Over most of Europe, negative skewness of DTD prevails in summer, while positive skewness dominates in winter. The asymmetry is reflected in small temperature increases occurring more often than small temperature decreases in summer, the opposite holding for winter. Unlike for temperature itself, mechanisms governing the asymmetry of DTDs have only been hypothesized but investigated neither in sufficient detail nor on scales larger than local.We employ the Jenkinson-Collison (JC) method to classify atmospheric circulation for grid points covering Europe and North Atlantic. The version of the JC method with 11 types (8 directional, 2 (anti)cyclonic, and one with weak pressure field) is adopted. For each grid point, we identify circulation types with the largest asymmetry of small DTDs (i.e., with the largest difference between the number of small day-to-day warmings and coolings). ‘Small’ is defined here as the inner 50% of values of a DTD distribution. Although the circulation types most conducive to the DTD asymmetry vary regionally, there is a pronounced tendency for the anticyclonic type, the type with a weak pressure field, and types with warm advection from the southern quadrant to contribute to the DTD asymmetry in summer. In winter, the anticyclonic type and types with cold northerly to easterly advection largely contribute to the DTD asymmetry.Next, we analyze surface energy budget, and particularly radiation and energy fluxes, for the circulation types conducive to the DTD asymmetry at selected representative grid points. We demonstrate that it is indeed radiative processes that strongly contribute to the prevalence of small day-to-day warmings in summer and of small day-to-day coolings in winter.We use ERA5 reanalysis as input dataset, with grid step of 1.25° x 1.25°. Sea level pressure is used as a circulation variable. Maximum temperature in summer and minimum temperature in winter are used to describe DTD. Analyzed period is 1940-2022.
A multilevel analysis of public risk perceptions of climate and weather
Authors: Thompson Jack , Dessai Suraje , Jenkins Sarah , Siu Yim Ling , Summers Barbara , Taylor Andrea
Published: July 2024

Public risk perceptions regarding climate and weather are known to be influenced by demographic factors including direct personal experiences with weather events. These perceptions are dynamic, often shaped by the immediacy and visual impact of weather phenomena and climate-related disasters, alongside evolving scientific understanding and projections of future climatic trends and their potential impacts on the environment and human society. In this paper, we present a multilevel analysis of public risk perceptions on climate and weather, leveraging the 2021 wave of the Lloyd’s Register Foundation World Risk Poll (WRP), a large survey dataset of 129,992 individuals interviewed across 121 countries. Our project aim is to test whether individual-level factors such as age, gender, education, household income, household size, whether their living environment is urban or rural, and personal experiences of severe weather events shape public risk perceptions on climate and weather. In this study, we also integrate several country level indices into the WRP dataset to generate insight on the relative influence of individual-level versus national-level factors in influencing our outcomes, including EM-DAT data on natural disasters (the number of climatological, geophysical, hydrological, and meteorological disasters in each country), ND-GAIN data on projected changes in hazards (future projections for heatwaves, flooding, and sea-level rises in each country), and WGI data on governance in each country. Using multilevel modelling techniques, we provide empirical validation of the effects of a list of factors that are theorised to shape climate and weather-based risk perceptions. The addition of random slopes (i.e. the intercepts is the same for all countries, but the slopes vary for each nation state) to our multilevel models also allows us to examine how the nature of the relationship of the identified factors and our outcomes varies by national context. The current findings highlight the dynamics between individual factors and larger environmental and governance contexts in shaping public risk perceptions of climate and weather. They form the basis for the future phases of our project development, which include the selection of several countries, representing different climate and social contexts, to use as case studies to test the effectiveness of early warning systems for disaster risk reduction. Our study and research findings offer important insights both for policymakers in crafting more effective, context-sensitive strategies for climate mitigation and adaption, and for national meteorological services in developing early warning systems for severe weather events.
Analysis of spatio-temporal droughts using order statistics of remotely sensed relative productivity index 
Authors: Bargaoui Zoubeida , Abid Nesrine
Published: July 2024

At the annual scale, the long-term average of the ratio of actual to potential evapotranspiration, represents the so-called relative productivity index (kv) which depends on climate, soil, and vegetation conditions. We aim to assess drought based on the analysis of the spatio-temporal variability of kv. The study area is the governorate of Zaghouan, located northern Tunisia, between latitudes 36°00N and 36°40N and longitudes 9°35E and 10°25E, mainly occupied by cereal crops. It is composed by 48 administrative districts (Imadas) of size between 20 and 80 km². Field evidence of drought is obtained using the National reports on cereal crops drought assessment. Bank facilities are provided to farmers with crop lands reported as drought damaged areas. The study period is 2000-2001 to 2020-2021. During that period, 11 years out of 21 were declared drought in the national reports. The methodology adopts order statistics of the remote sensing kv. The latter is estimated by imada, using MODIS data, considering the accumulation of evapotranspiration during the cereal growth period, from November to May (herein year by extension). Thus, a kv matrix of N=21 columns (years) with n=48 raws (imadas) is analyzed. For a given year i, we consider the values of kv arranged in increasing order of magnitudes (X(i,1), X(i,2), …, X(i,n)) where n is the number of imadas. Four drought prognostics are evaluated. We see whether the droughts registered by the National reports are retrieved by the prognostic methods. The minimum of kv (X(i,1)) is evaluated in Methods 1 while the maximum X(i,n) is tested for Method 2. In Method 1 the 25% percentile of X(1) is considered to filter drought years. In Method 2 and Method 3, the 75% percentile of X(n) and 2.5% percentile of X(n/2) are respectively considered. In Method 4, a quantile-quantile plot approach is adopted based on the minimization of mean absolute error, considering X(10%), X(25%), X(50%) and X(75%). Four categories of years are assumed in Method 4: severe drought; moderate drought; as well as mild humid and humid (no drought). Method 1 resulted in one single undetected drought year. Method 2 resulted in 2 false droughts and one drought not detected. In Method 3 one single drought is false. Method 4 resulted in three drought years that were classed as humid. Though, these results encourage the use of order statistics of satellite relative productivity index as a procedure for drought identification over large areas.
Model Study on Urban Aerosol-Cloud Interactions and their Influence on Precipitation
Authors: Bär Friederike , Quante Markus , Matthias Volker , Heinold Bernd
Published: July 2024

Besides influencing dynamics, cities are major sources of anthropogenic aerosols. Activated as cloud droplets they can modify the cloud microphysical processes, which can lead to an intensification, delay or even suppression of convective precipitation. However, it is hard to separate the urban cloud aerosol effect from roughness and thermal effects related to the urban morphology and variability in meteorological conditions. Therefore, quantifying this effect is challenging due to the relatively small signal amid high variability.In this study, we aim to better understand how urban emissions affect clouds and thereby precipitation processes. For this purpose, we use fine resolution simulations including an urban parametrization and a direct aerosol-cloud coupling. use the Consortium for Small-Scale Modeling (COSMO) model online-coupled to the multi-scale chemistry aerosol transport model MUSCAT and the double-canyon urban canopy parameterization DCEP. To simulate a direct aerosol-cloud-precipitation coupling we modified the 2-moment bulk microphysics scheme from COSMO to allow cloud condensation nuclei to be calculated directly from aerosol mass concentrations simulated with MUSCAT, instead of assuming constant cloud condensation nuclei concentrations. A 3-way nesting strategy is applied for the simulations, with the highest resolution area centered over the Leipzig metropolitan area, using a 1km grid spacing. We employ this setup in a case study on a small-scale convective storm that passed over the City of Leipzig. Sensitivity tests have been performed to investigate how sensitive the simulated precipitation is to changing aerosol concentrations, by varying the input emissions. First results show that a doubling of emissions weakens and shifts the location of maximum precipitation further downstream the urban area. Additional simulations with chemistry boundary values set to zero enable us to assess how urban emissions are transported vertically and how they can influence the convective storm. Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany‘s Excellence Strategy – EXC 2037 'CLICCS - Climate, Climatic Change, and Society' – Project Number: 390683824 
Links between weather variability and Dengue outbreaks in Sao Paulo, Brazil
Authors: Urban Aleš , Araújo Júlia , Oliveira Sheila , S. Celina Seyma , Černý Jiří
Published: July 2024

Mosquito-borne diseases are among the most dangerous threats for all people living in tropical areas and Dengue fever is one of the fastest growing disease in the world (Watts et al. 2019). Previous research has shown that the highest incidence of mosquito-borne diseases is associated with a particular type of weather (usually wet and hot) as mosquitos’ activity and development are highly dependent on meteorological conditions. However, short-term associations (on the scale of days up to a few weeks) have been less understood.In this study, we collected weekly data on the incidence of Dengue on a municipality level (obtained from Secretaria de Estado da Saúde de São Paulo). These were aggregated into 17 Regional Health Departments (DRSs) in the state of Sao Paulo, Brazil, 2016–2022 and standardized by population count in each DRS. Additionally, environmental and socioeconomic characteristics of the 17 DRSs were collected and matched with ERA5-based weather characteristics (ambient temperature, relative humidity, wind speed and precipitation). Consequently, we employed a mixed meta-regression model with a random effect in order to analyse the links between Dengue incidence and weather variables, while taking into account the modifying effects of environmental and socioeconomic characteristics.Our preliminary results suggest the largest relative risk of Dengue incidence at week 0 to 3 after a warm and humid period and up to 12 weeks after a heavy rainfall. Further analysis is needed to identify spatial differences in these patterns based on socioeconomic conditions. This study will contribute to better understanding of short-term links between weather variability and Dengue outbreaks.Watts, N et al. (2019). The 2019 report of The Lancet Countdown on health and climate change: ensuring that the health of a child born today is not defined by a changing climate. The Lancet, 394(10211), 1836–1878. https://doi.org/10.1016/S0140-6736(19)32596-
Severe drought events of late 19th century in Catalonia (NE Spain) and their social impacts (1864-1897)
Authors: Barriendos Josep , Piera Gerard , Gorostiza Santiago , Barriendos Mariano
Published: July 2024

Drought is a recurrent feature of the Mediterranean climate. However, some of its severe manifestations cause important social impacts. In the current context of climate change, Catalonia (NE of the Iberian Peninsula) is experiencing episodes of drought with a frequency and severity of worrying characteristics. The study of this climatic risk on large time scales is justified in order to enable better management of any similar situations that may occur in the future.This work proposes an approach to some of the episodes of similar severity that have occurred in recent history. This allows us to incorporate information from different sources. Specifically, the study analyses the period of the last third of the 19th century, in which a sustained rainfall deficit appears in the instrumental meteorological series of Barcelona. From this first identification, we work with the instrumental series available in Spain before 1900. We use a total of 18 rainfall series that covers the last third of the 19th Century, including that of Barcelona (1786-2023). We also use information from historical documentary sources for the period 1860-1900, which contain cultural proxies (pro pluvia rogations) and direct descriptions of drought impacts.A high temporal resolution study, with information at daily resolution, makes it possible to study the conditions and evolution of the drought during the aforementioned period on the Spanish Mediterranean coast and in Catalonia. It identifies the periods of greatest severity and the areas affected.Moreover, the utilisation of documentary and bibliographical sources allows us to identify and evaluate information on the impacts suffered by society and the stages of deterioration that are progressively detected. The episode of drought at the end of the 19th century may not manifest acute and evident periods, but its long duration generated a serious social situation. Agricultural activity suffered from low yields and this productive crisis was showed by the markets. Resulting in severe tensions and conflicts amongst the working classes, both rural and urban, due to these economic problems. This situation led to a strong deterioration of the socio-political situation that continued during the first years of the 20th century.
Enhancing global wind climate simulations in the GFDL-AM4 model by unifying planetary boundary layer and cloud turbulence parametrizations with the higher-order scheme CLUBB
Authors: Gentile Emanuele Silvio , Zhao Ming , Larson Vincent , Zarzycki Colin
Published: July 2024

The higher-order turbulence scheme, Cloud Layers Unified by Binormals (CLUBB), is known for effectively  simulating the transition from cumulus to stratocumulus clouds within leading atmospheric climate models.  Here we investigate an underexplored aspect of CLUBB: its capacity to simulate near-surface winds and the Planetary Boundary Layer (PBL), with a particular focus on its coupling with surface momentum flux, modelling of turbulent lengthscale, and direct prognosis of turbulent momentum flux. First, using the GFDL atmospheric climate model (AM4), we examine two distinct coupling strategies, distinguished by their handling of surface momentum flux during the CLUBB’s stability-driven substepping performed at each atmospheric time step.  The static coupling maintains a constant surface momentum flux, while the dynamic coupling adjusts the surface momentum flux at each CLUBB substep based on the CLUBB-computed zonal and meridional wind speed tendencies. Our 30-year present-day climate simulations (1980-2010) show that static coupling overestimates 10-m wind speeds compared to both control AM4 simulations and reanalysis, particularly over the Southern Ocean (SO) and other midlatitude ocean regions. Conversely, dynamic coupling corrects the static coupling 10-m winds biases in the midlatitude regions, resulting in CLUBB simulations achieving there an excellent agreement with AM4 simulations. Furthermore, analysis of PBL vertical profiles over the SO reveals that dynamic coupling reduces downward momentum transport, consistent with the found wind-speed reductions. Instead, near the tropics, dynamic coupling results in minimal changes in near-surface wind speeds and associated turbulent momentum transport structure. Then, implementing a more generalized calculation of the turbulent length scale leads to an overall degradation of winds but improves root mean square error and bias of low cloud amount in key regions of stratocumulus to cumulus transition. Remarkably, we show that updating CLUBB formulation of momentum flux from diagnostic to prognostic, to permit countergradient momentum fluxes, improves nearly everywhere global winds, while retaining the benefits of the more generalised lengthscale formulation in accurately capturing the low cloud amount. Finally, the wind turning angle serves as a valuable qualitative metric for assessing the impact of changes in surface momentum flux representation on global circulation patterns.  
Current state of TV weather in the USA   
Authors: Trobec Jay
Published: July 2024

Approximately 1,100 television stations in the USA broadcast local news. Most – if not all – also broadcast weather information. According to a comprehensive 2023 survey, the “average” television station employs four weather people. Over the past five years, the number of active, degreed television presenters who hold the American Meteorological Society’s “Certified Broadcast Meteorologist” designation – the most recognized television weather certification – has shown steady growth, with the number of CBM’s now above 600. Additionally, over 400 weather presenters hold the National Weather Association Seal of Approval. While the public now has access to many sources of weather forecasts (e.g., phone apps, websites), television is still a substantial source of weather information. During periods of severe and high-impact weather, television dominates weather communication. Technological advances such as remote broadcasting capabilities, inexpensive remote weather cameras, and virtual studio weather sets have made television weather presentations more compelling than ever before. 
Interpretable machine learning reveals the drivers of turbulence anisotropy over complex terrain.
Authors: Mosso Samuele , Lapo Karl , Stiperski Ivana
Published: July 2024

Turbulence anisotropy has recently gained attention for its role in the study of surface layer turbulence. In particular the degree of anisotropy, quantified through the anisotropy invariant yB, has been successfully introduced as an additional non-dimensional parameter into the Monin-Obukhov Similarity Theory (MOST), and tested on the flux-variance and flux-gradient surface scaling relations. The novel extended MOST relations were shown to explain the observed scatter in the MOST scaling relations both over flat and highly complex terrain, thus allowing MOST to be extended outside of its restrictive original assumptions. The challenge, however, still remains in how to predict yB for a range of realistic conditions, which would allow to implement the novel scaling relations in numerical models’ surface parametrizations.In this study we use data from both flat (AHATS) and complex terrain (Perdigao measurement campaign), to understand the drivers of turbulence anisotropy. We use interpretable machine learning techniques considering a wide range of macro and micro meteorological variables, surface heterogeneity, and topographic measures to build a predictive model using a tree-based regression algorithm (i.e. random forest and boosting algorithms). Interpretability techniques, such as variable importance measures, partial dependence plots, and Shapley analysis then allow us to select the variables that influence turbulence anisotropy the most and assess their relation with each other and with our target variable. This approach will ultimately lead to an understanding of the processes behind the emergence of different states of turbulence and its anisotropy, paving the way for robust surface parametrizations in numerical weather models.
Relationship between Doppler wind lidar observed wind speed and Carrier-to-Noise ratio
Authors: Gryning Sven-Erik , Kayser Markus , O’Connor Ewan , Batchvarova Ekaterina
Published: July 2024

We investigate the relationship between the CNR and the mean horizontal wind speed estimated by a Doppler lidar. Intuitively, high values of CNR represent high concentration of particles that backscatter the light beam and therefore denote observations with high accuracy, while decreasing values of the CNR gradually give rise to a higher uncertainty. For a Leosphere V2 Doppler wind lidar that was operated in DBS mode in the high Arctic, we observed three distinct ranges of the normalized mean wind speed along the range of CNR values. In the CNR range from ≈ -14 dB to -22 dB there is a decrease of about 5% in the observed mean horizontal wind speed. It is very fortunate that the decrease is followed by a flat plateau of the wind speed for CNR values between -22 dB and -26 dB in which the estimate of the wind speed is not sensitive to the choice of the CNR threshold value. An abrupt increase in the normalized wind speed for CNR values < -26 dB signifies that the backscattered signal becomes weak due to low aerosol concentrations and noise starts to corrupt the estimation of the mean wind speed. This characteristic pattern was also found in observations at the Meteorological Observatory Lindenberg using a WindCube 200s and a Stream Line XR system, both operating in VAD mode. But the specific CNR values separating the three distinct ranges are not quite the same. Generally, the flat plateau and the subsequent rapid increase as CNR values decrease further are well known and are a consequence of the detector sensitivity with respect to the noise level; this feature can help operators to identify a filter threshold suitable for their particular instrument. However, the decrease of the wind speed for CNR values between -14 dB and -22 dB deserves further investigation.Based on the two data sets of Doppler wind lidar observations, the decrease in the wind speed for CNR values, as well as the flat plateau will be presented and discussed. The CNR dependence on the observed wind speed is likely instrument dependent but can also have a sensitivity to height as well as meteorological conditions.
Indoor and Outdoor Population Prediction Using Location-Based Services Based on Machine Learning Approach
Authors: Suzuki Tomoki , Kimura Teru , Ohashi Yukitaka , Takane Yuya , Yamaguchi Kazuki , Ihara Tomohiko
Published: July 2024

In the fields of research concerning heatstroke, air pollution, and disasters, it is necessary to distinguish between indoor and outdoor populations in order to evaluate the benefit of different measures indoors and outdoors. However, there are few studies such distinctions have been taken into account because it is difficult to obtain the dataset of the distribution of indoor and outdoor populations.This study predicts the ratio of indoor populations on a 500 m grid basis in Tokyo by machine learning approach. In this prediction, time-series location data, building area, building attributes and temperature is used as explanatory variables and the ratio of indoor populations is set as objective variables. In particular, it focuses on improving the accuracy of predictions for times when the ratio of indoor to outdoor populations stabilizes, which is critical for assessing risks such as heatstroke.The machine learning model, employing Random Forest, demonstrated high predictive accuracy with an average error of 4%. Additionally, the model's performance improved during morning commute hours, lunch breaks, and evening return times. Also, the effects of various factors such as whether it is a holiday, convenience of transportation, and commercial activities are investigated in terms of the respective increases and decreases in indoor and outdoor populations.While our methods have some limitations such as accuracy of location data, and the scarcity of data points, these results are expected to have broad social applications in the future, including risk assessments for heatstroke, air pollution, and disasters.Future work includes establishing more robust validation methods, using more accurate time-series location data, and adding explanatory variables that better represent the characteristics of each grid. This would lead to even more accurate predictions.
Potential Vorticity Circulation Crossing the Boundaries of Northern Troposphere and the East Asian Monsoon in July
Authors: Wu Guoxiong , Liu Yimin , He Bian , Sheng Chen , Li Yanxi
Published: July 2024

The source of potential vorticity (PV) substance (PVS) of the atmosphere is at the earth’s surface, whereas the PVS variation within a limited region depends on the sum of the effective PV flux crossing the boundary surface surrounding the region. Because the PV circulation (PVC) is the time- integral of effective PV flux, the monthly mean gross PVS in the northern troposphere is determined by the sum of PVC penetrating the enveloped three boundaries, e. i., the PVC cross- the equatorial vertical section CEPVC, the PVC cross- the tropopause CUPVC, and the PVC cross- the earth’s surface CBPVC. Because the CBPVC is associated with surface potential temperature and circulation, for an equilibrium gross PVS in the hemisphere, the change in CEPVC or/and CUPVC can induce the climate change near the surface through the adjustment of the atmospheric internal PVC changes.Based on reanalysis data and by employing statistics and diagnostics, the characteristics of the July mean PVS and PVC in the northern troposphere, and the relationship between the Asian monsoon and the PVC crossing its three boundaries in July are investigated. Results demonstrate that, corresponding to a CBPVC pattern with negative (positive) PVC over the north (south) on the Tibetan Plateau, strong positive PV anomalies and westerly flows develop in the troposphere over the plateau， leading to increased precipitation in the downstream area along the Meiyu front. The combination effects of the CEPVC and CUPVC can trigger the adjustment of the troposphere internal PVC, resulting in PVS and circulation redistribution within NH and forming a meridional seesaw of precipitation over the East Asian summer monsoon area. Attribution diagnoses reveal that the tropical sea surface temperature anomaly contributes significantly to the CEPVC, whereas the meridional gradient of PVS over the northwestern Tibetan Plateau on the tropopause contributes more to the CUPVC.
Homogenization of Daily Wind Speed and Wind Gust Time Series in the Era of Automatic Observations in Catalonia
Authors: Domonkos Peter , Prohom Marc , Cunillera Jordi
Published: July 2024

The homogeneity of wind speed and wind gust data is very sensitive to changes in the instrumentation or the environment of the sensor. Time series of wind observations over Catalonia have been analysed, and in spite of the relatively short history of automated observations, several significant inhomogeneity biases were detected and removed.A dense network of observing stations and documentation of station histories (metadata) helped the homogenization. Only time series with observation periods of at least 10 years were used, resulting in the homogenization of 209 time series for both wind speed and wind gust. The data was originated from three sources, i.e. the primary network of the Catalan Meteorological Service (SMC), the agrometeorological observation network of the SMC and the network of the Spanish Meteorological Agency (AEMET) in Catalonia.The data underwent basic quality control before homogenization. Homogenization was performed with ACMANT, known to be the most accurate homogenization method based on currently available method comparison test results. The used new version ACMANTv5.2 can take the benefit of metadata either in automatic or interactive mode. Prior to homogenization, a specific examination was performed, in which residual standard deviations of two inhomogeneity models were compared, i.e., the additive model and multiplicative model. The additive model showed clear advantages for wind gust homogenization, while the suitability of the two models appeared to be similar for the homogenization of mean wind speed data. Metadata indicated several technical changes during the observation periods of 24 years on average. Most stations experienced changes in sensors and data logger multiple times, while a few time series were affected by station relocations or changes in the sensor height.The homogenization process revealed that changes in sensors and data logger often did not cause perceptible inhomogeneities. On the other hand, two network-wide changes in data loggers occurred, in 2005 in the red agrometeorological network and in 2007 in the primary network of Meteocat, when the technical changes impacted significantly and almost synchronously the homogeneity of the data. Since all relative homogenization methods, including ACMANT, presume that inhomogeneities are station-specific, metadata played a crucial role in detecting these problems. The final homogenization was performed in a way that neighbour series affected by the same type inhomogeneity bias as that of the candidate series were excluded from the homogenization. 
Modeling secondary organic aerosol over urban areas of Central Europe: uncertainties linked to different mechanisms and parameterizations
Authors: Bartík Lukáš , Huszár Peter , Karlický Jan , Vlček Ondřej
Published: July 2024

The most significant environmental health risk for the European population is air pollution, especially for people living in urban areas. Fine particulate matter (PM), a mixture of aerosols with an aerodynamic diameter less than or equal to 2.5 μm, is among the pollutants with the most critical threat to human health in European urban areas. Despite the substantial spatial and temporal variability of the chemical composition of fine PM in Central Europe, it is generally dominated by organic matter and secondary inorganic aerosols, with organic matter being the main contributor to total submicron PM. Modeling organic aerosol using chemical transport models (CTMs) has been problematic for decades, with CTMs often underestimating its concentrations. Potential sources of this deficiency in CTMs include:(1) simplifying assumptions applied in the model description of organic aerosol,(2) uncertainties in the model mechanisms describing gas phase chemistry since these mechanisms determine the concentrations of gaseous precursors of secondary organic aerosol,(3) missing emissions of intermediate-volatility and semivolatile organic compounds (IVOCs and SVOCs) in emission inventories used in CTM simulations. In this work, we focused on investigating the impacts of these sources of uncertainty on the concentrations of organic aerosol in six large cities of the Central European region (Prague, Vienna, Budapest, Berlin, Munich, and Warsaw). For this purpose, we performed a series of model simulations employing an offline coupled modeling framework consisting of the Weather Research and Forecast (WRF) Model and the Comprehensive Air quality Model with Extensions (CAMx) on the Central European domain with a horizontal resolution of 9 km for the period covering the years 2018 and 2019. More specifically, we focused on assessing the influence of two mechanisms for organic aerosol (SOAP, 1.5-D VBS), three mechanisms of gas-phase chemistry (CB6r2, CB6r5, and SAPRC07TC), and several different source-specific parameterizations of IVOCs and SVOCs.
Value added by hazard impact modelling within the warning value chain for three impactful surface water flooding events in 2022
Authors: Neal Robert , Robbins Joanne , Mooney John , Egan Katie , Perez Julia
Published: July 2024

The warning value chain is a concept whereby the process of producing a warning is represented by a chain of sources of expertise (components), connected by bridges that convey bidirectional information exchanges. An associated warning value chain questionnaire – produced by the WMO’s High Impact Weather (HIWeather) Warning Value Chain Flagship Project – has been used to carry out a warning value chain assessment for three surface water flooding (SWF) events in the UK. These events occurred during the summer and autumn of 2022 and involved localised convection leading to predominantly minor SWF impacts. Forecast confidence was very low for all events resulting in some short-lead time and responsive warnings. This talk will briefly describe these events before focusing on the value added to the warnings by the Surface Water Flooding Hazard Impact Model (SWFHIM). SWFHIM is an operational forecasting tool used by the Flood Forecasting Centre (a partnership between the Met Office and Environment Agency) to objectively assess the likelihood of SWF impacts across England and Wales, which ultimately feeds into the decision-making process for issuing warnings. The SWFHIM adds value to the weather and hazard outputs by deriving probabilities of impacts using a range of post-processing and forecast presentation methods. However, results from the warning value chain questionnaire helped to identify improvements that may enhance future warnings. For example, SWFHIM forecast presentation changes may improve the pull-through and interpretation of information. Also, recent enhancements to the configuration of the driving convective-scale ensemble are not yet being fully utilized by the hazard-impact modelling. These points and more will be discussed in this talk. 
GIS-aided historical data for assessing rainfall-related effect along a transportation network of a coastal Mediterranean area (Calabria, southern Italy)
Authors: Scarcella Graziella Emanuela , Conforti Massimo , Cosentini Giovanni , Valente Enzo , Petrucci Olga
Published: July 2024

We present the results of an historical investigation focusing on rainfall-related impact on the transportation network of a coastal Mediterranean area (Calabria, southern Italy) recorded throughout 220 years. The entire series of documentary data gathered allowed us to identify the most frequently and/or severely hit sectors, and GIS elaboration was performed to produce a vulnerability map of the study area, highlighting sectors most prone to be affected and the most probable kind of hazard to be expected as impact source.On the other side, the 220-year damage series and its comparison with daily rainfall data, available since 1938, allowed us to identify rainfall thresholds able to trigger either severe territorial crisis or temporary and light impact on transport facilities, and to identify the long-term temporal/seasonal trend of past damaging events. The study area, around 70 km2 wide, is the sum of the surface of two Calabrian municipalities (Scilla and Bagnara Calabra) located in the southernmost regional province of Reggio Calabria. Beside to historical data about landslides, floods, and storm surges contained in the database ASICal (Italian acronym of historically flooded areas), a data catalogue maintained by CNR-IRPI researchers, we collected further data performing specific surveys in local State archives and in the website of the companies which own and maintain roads and railways of the area, as well as in press archives of regional and national newspapers.The total number of damage-related reports collected was about 280 over 220 years. Damage caused by landslides (85 %) was more numerous than those caused by floods (15 %). Probably due to the largest number of data sources characterizing most recent years, the highest event’s occurrence (27%) pertains to the decade 2010-2019, with a seasonal distribution higher in autumn-winter period (36%). The study delves into the pros and cons of the use of historical data and their usefulness for the identification of critical points and management of territorial crisis in the light of climate change in a Mediterranean study area.This work was funded by the Next Generation EU—Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of ‘Innovation Ecosystems’, building ‘Territorial R&D Leaders’ (Directorial Decree n. 2021/3277)—project Tech4You—Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors' views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.