National Meteorological Service Research Articles

Weather Forecasting in Brazil: A Concise Historical Review

The main objective of this article is to describe the factors and issues responsible for the evolution of the weather forecast in Brazil.This is done based on a historical review of the formation and evolution of the national meteorological services in the last 170 yearsand on the development of weather forecasting methods. Changes in the routines of weather forecasting services in two centenaryBrazilian institutions, the National Institute of Meteorology and the Brazilian Navy, since the creation of the first subjective forecaststo the present day, are highlighted. Information about the 14 undergraduate courses in Meteorology in Brazil is given, which supportthe technological development of this science, through scientific research and training of human resources. The introduction ofmeteorological radar in the 1970s, and its current networks, as well as the elaboration of the first numerical weather predictions (NWP)by the Center for Weather Forecasting and Climate Studies (Centro de Previsão do Tempo e Estudos Climáticos do Instituto Nacionalde Pesquisas Espaciais – CPTEC/INPE), in 1995, are also described. To complement, a survey is presented, showing the currentworking conditions of weather forecasters. The survey results reveal that 45% of the 102 meteorologists interviewed use the CzechRepublic Windy application to prepare their weather forecasts operationally and almost 60% use the Wyoming University website toobtain data from radiosondes launched in Brazil. It is important to highlight that, since the introduction of NWP by CPTEC/INPE, at theend of the 1990s, there has been a great advance in the field of weather forecasting. Moreover, observational networks have undergonea great expansion, with a significant increase in the number of weather stations in recent decades. Despite all the progress achieved,there is still a need for the integration of observational networks and databases of various institutions. Finally, the development ofapplications that meet the demand of young meteorologists in the operational centers is advisable.

Overview: Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes

Abstract. Cloud and precipitation processes are still a main source of uncertainties in numerical weather prediction and climate change projections. The Priority Programme “Polarimetric Radar Observations meet Atmospheric Modelling (PROM)”, funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG), is guided by the hypothesis that many uncertainties relate to the lack of observations suitable to challenge the representation of cloud and precipitation processes in atmospheric models. Such observations can, however, at present be provided by the recently installed dual-polarization C-band weather radar network of the German national meteorological service in synergy with cloud radars and other instruments at German supersites and similar national networks increasingly available worldwide. While polarimetric radars potentially provide valuable in-cloud information on hydrometeor type, quantity, and microphysical cloud and precipitation processes, and atmospheric models employ increasingly complex microphysical modules, considerable knowledge gaps still exist in the interpretation of the observations and in the optimal microphysics model process formulations. PROM is a coordinated interdisciplinary effort to increase the use of polarimetric radar observations in data assimilation, which requires a thorough evaluation and improvement of parameterizations of moist processes in atmospheric models. As an overview article of the inter-journal special issue “Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes”, this article outlines the knowledge achieved in PROM during the past 2 years and gives perspectives for the next 4 years.

Access, Uptake, Use and Impacts of Agrometeorological Services in Sahelian Rural Areas: The Case of Burkina Faso

Agrometeorological services, as part of weather and climate services, are expected to play a key role in supporting sub-Saharan agriculture facing climate change and variability. In the Sahel, smallholder farmers relying on rainfed crop production systems are particularly vulnerable to climate change and variability because of low resilience and coping capacity. The provision of agrometeorological services is growing across Africa, but they often remain inaccessible for the majority of smallholder farmers or are not very relevant to support on-the-ground decision-making. Our work aims to demonstrate the hypothesis that agrometeorological services can effectively improve agricultural productivity and sustainability provided that appropriate mechanisms are put in place to ensure access, uptake and action. The paper illustrates the case study of Burkina Faso, where the National Meteorological Service, with the support of the World Meteorological Organization, engaged in the provision of accessible, reliable and relevant agrometeorological services for farmers. The study demonstrates that farmers, even in remote rural areas, are willing to profit from weather and climate services for strategic and tactical decisions in agricultural management because of relevant economic benefit. These benefits can be summarized as a 40% reduction in production costs and a 41% increase in income. Results also highlight environmental positive impacts such as the reduction by 50% in the use of fertilizers. Nevertheless, the study concludes that in order to scale-up weather and climate services in West Africa, a new business model released from the development projects approach should be explored.

Intraregional resettlement scheme and livelihood perspectives in Southwestern Ethiopia: challenges and prospects

ABSTRACT This study investigates the livelihood perspectives of resettlers, challenges and prospects of intra-regional resettlement scheme in Chewaka district of Ethiopia. Through multistage sampling procedure, 384 households were selected from sample kebeles for household survey. Descriptive and inferential statistics along with Household Food Balance Model have been employed to analyze data. The results revealed that resettlement scheme had brought substantial changes on livelihood of the resettled communities. It is successful in rescuing human life, improving livelihood assets and ensuring food security for most of the settlers. The study suggests urgent attention on improving infrastructures and environment conservations for betterment of livelihoods. Abbreviations: BZARDO: Buno Bedelle Zone Agriculture and Rural Development Office; DAs: Development Agents; TM: Thematic Mapper: ETM+: Enhanced Thematic Mapper Plus; NMSA: National Meteorological Service Agency; SNNPR: Southern Nations, Nationalities and Peoples Region

EUNADICS-AV early warning system dedicated to supporting aviation in the case of a crisis from natural airborne hazards and radionuclide clouds

Abstract. The purpose of the EUNADICS-AV (European Natural Airborne Disaster Information and Coordination System for Aviation) prototype early warning system (EWS) is to develop the combined use of harmonised data products from satellite, ground-based and in situ instruments to produce alerts of airborne hazards (volcanic, dust, smoke and radionuclide clouds), satisfying the requirement of aviation air traffic management (ATM) stakeholders (https://cordis.europa.eu/project/id/723986, last access: 5 November 2021). The alert products developed by the EUNADICS-AV EWS, i.e. near-real-time (NRT) observations, email notifications and netCDF (Network Common Data Form) alert data products (called NCAP files), have shown significant interest in using selective detection of natural airborne hazards from polar-orbiting satellites. The combination of several sensors inside a single global system demonstrates the advantage of using a triggered approach to obtain selective detection from observations, which cannot initially discriminate the different aerosol types. Satellite products from hyperspectral ultraviolet–visible (UV–vis) and infrared (IR) sensors (e.g. TROPOMI – TROPOspheric Monitoring Instrument – and IASI – Infrared Atmospheric Sounding Interferometer) and a broadband geostationary imager (Spinning Enhanced Visible and InfraRed Imager; SEVIRI) and retrievals from ground-based networks (e.g. EARLINET – European Aerosol Research Lidar Network, E-PROFILE and the regional network from volcano observatories) are combined by our system to create tailored alert products (e.g. selective ash detection, SO2 column and plume height, dust cloud, and smoke from wildfires). A total of 23 different alert products are implemented, using 1 geostationary and 13 polar-orbiting satellite platforms, 3 external existing service, and 2 EU and 2 regional ground-based networks. This allows for the identification and the tracking of extreme events. The EUNADICS-AV EWS has also shown the need to implement a future relay of radiological data (gamma dose rate and radionuclides concentrations in ground-level air) in the case of a nuclear accident. This highlights the interest of operating early warnings with the use of a homogenised dataset. For the four types of airborne hazard, the EUNADICS-AV EWS has demonstrated its capability to provide NRT alert data products to trigger data assimilation and dispersion modelling providing forecasts and inverse modelling for source term estimate. Not all of our alert data products (NCAP files) are publicly disseminated. Access to our alert products is currently restricted to key users (i.e. Volcanic Ash Advisory Centres, national meteorological services, the World Meteorological Organization, governments, volcano observatories and research collaborators), as these are considered pre-decisional products. On the other hand, thanks to the EUNADICS-AV–SACS (Support to Aviation Control Service) web interface (https://sacs.aeronomie.be, last access: 5 November 2021), the main part of the satellite observations used by the EUNADICS-AV EWS is shown in NRT, with public email notification of volcanic emission and delivery of tailored images and NCAP files. All of the ATM stakeholders (e.g. pilots, airlines and passengers) can access these alert products through this free channel.

Identifying and Using Suitable Oceanic Domains for Prediction of the Indian Monsoon Rainfall over Tamil Nadu

Rainfall prediction are vital for agriculture which is one of the primary sectors greatly affected by climate variability and extremes. Agriculture plays a vital role in shaping the economy of India which is often affected by monsoon. Sea surface temperature (SST) plays a vital role in rainfall predictability over the land surface. A total of twelve different domains of oceanic influences of SST on monsoon rainfall over Tamil Nadu were selected for analysis. The SST of different lead times (February, March, April, and May for southwest monsoon (SWM) and June, July, August, and September for northeast monsoon (NEM) from the ERSSTv5 and ECMWF-SEAS5 model with the Canonical Correlation Analysis (CCA) were used in the Climate Predictability Tool (CPT) to identify the best predictor domains for the prediction of SWM and NEM rainfall over Tamil Nadu. The model training utilized the first 40 years (1981-2020) SST and rainfall data and prediction was done for the 2021 seasons. The results of the study revealed from Kendall tau goodness index and CCA score, the predictor domains comprised of a combination of oceanic domains, this were the Indian, Arabian, Bay of Bengal, and Pacific Oceans recorded the best CCA score and the goodness index. Is therefore recommended that, these domains which have the highest overall predictability can be used by the National meteorological services to early warning and monsoon rainfall information over Tamil Nadu.

The Status and Future of Small Uncrewed Aircraft Systems (UAS) in Operational Meteorology

AbstractThe boundary layer plays a critical role in regulating energy and moisture exchange between the surface and the free atmosphere. However, the boundary layer and lower atmosphere (including shallow flow features and horizontal gradients that influence local weather) are not sampled at time and space scales needed to improve mesoscale analyses that are used to drive short-term model predictions of impactful weather. These data gaps are exasperated in remote and less developed parts of the world where relatively cheap observational capabilities could help immensely. The continued development of small, weather-sensing uncrewed aircraft systems (UAS), coupled with the emergence of an entirely new commercial sector focused on UAS applications, has created novel opportunities for partially filling this observational gap. This article provides an overview of the current level of readiness of small UAS for routinely sensing the lower atmosphere in support of national meteorological and hydrological services (NMHS) around the world. The potential benefits of UAS observations in operational weather forecasting and numerical weather prediction are discussed, as are key considerations that will need to be addressed before their widespread adoption. Finally, potential pathways for implementation of weather-sensing UAS into operations, which hinge on their successful demonstration within collaborative, multi-agency-sponsored testbeds, are suggested.

Comparative performance of HWRF model coupled with POM and HYCOM for tropical cyclones over North Indian Ocean

The demonstration of air-sea interaction in the high-resolution numerical weather prediction (NWP) model is the necessary condition to predict the extreme event like tropical cyclone. It is already established that the coupled mesoscale models are capable of forecasting the track and intensity of tropical cyclones with reasonable skill over North Indian Ocean (NIO) basin and over other ocean basins as well. The Hurricane Weather Research and Forecasting (HWRF) model is consequently operational by India Meteorological Department (IMD) for the forecasting tropical cyclones like many other National Meteorological and Hydrological Services. Two different coupled versions of HWRF modeling systems with feature based Princeton Ocean Model (POM) and eddy-resolving Hybrid Coordinate Ocean Model (HYCOM) are employed for the real-time forecasting of tropical cyclones over NIO. In this study, the comparison of forecast skills between two coupled variants in predicting track and intensity for all eight cyclones during 2019 has been carried out. The results show that the track prediction of all cyclones by HWRF model coupled with HYCOM is comparatively better than that with POM. But the intensity prediction skills of both coupled models do not portray any obvious superiority for all forecast hours. The absolute intensity errors of HWRF model with POM coupling are large compared to HYCOM during initial phase up to 60 hours of forecast except for Vayu cyclone. But, the errors in intensity exhibited by POM are a bit smaller compared to HYCOM for longer lead period. However, the standard deviation is very large for both models and numbers of forecasts verified are also less. In case of two cyclones of 2020, the ability of coupled variant with HYCOM is superior to POM in forecasting the tracks of cyclones. Considering the skills of both the models for intensity prediction, it is clear that the use of coupled HCOM model for short-lived weaker storms is advantageous but the performance of the model is not consistently maintained with longer lead time for intense and long-lived cyclone. A brief assessment of surface fluxes and Sea Surface Temperature reveals that HYCOM portray more realistic ocean condition in turn surface fluxes compared to POM and therefore the coupled variant of HWRF with HYCOM is superior to the variant with POM while forecasting cyclones over deep Ocean.

Climate information needs and services for climate change mitigation and adaptation in Cameroon, Kenya, Malawi, Nigeria and Tunisia

Poor access and use of accurate, timely and appropriate climate information consistently to inform decision making in African countries pose a huge dilemma for sustainable development. The existing climate data observations networks coverage are sparsely distributed. Development strategies and plans are not adequately informed by climate science due to the limited reliable and useable climate data and information produced by National Meteorological and Hydrological Services and other relevant institutions. This paper describes the climate information needs of Cameroon, Kenya, Malawi, Nigeria and Tunisia, the status of the climate information systems (CISs), gaps and recommends improvement in the generation, processing and use of climate information. A study involving in-depth desk studies, key informant interviews, focus group discussions and policy dialogue was conducted. Results revealed that the CISs in the target countries have weak forecasting and alert systems for weather events and low capacities of stakeholders in climate prediction, development of climate products and information to support long-term planning, climate adaptation and resilience. The quality and appropriateness of data collected needs to be improved through networking, development and use of innovative technologies and capacity building. Institutionalizing climate data management training within climate data collection stations, creating modern real-time CISs by strengthening the capacities of national and regional institutions to use and disseminate climate information, is paramount. The capacity of the ministries involved in climate data management to deploy appropriate climate information and best practices to effectively implement climate-proof policies and practices should be enhanced to increase climate resilience and productivity.
 Key words: climate information needs, climate service, adaptation, mitigation, capacity

Seasonal discharge forecasting for the Upper Danube

Study RegionThe authors contributed with a seasonal forecast system for discharge in the case study area of the Upper Danube basin upstream of Vienna. The basin, with a drainage area of approx. 100.000 km2, is characterized by complex topography dominated by the Alps. Study FocusNew advances in the mean term (seasonal) climate forecasting increase the potential of seasonal hydrological forecasting. In the frame of an European project (Clim2Power, 2020) a climate service for the assessment of seasonal energy production of renewable resources was developed. Seasonal discharge forecasts are generated using a dynamical approach running the hydrological model COSERO with forecasted climate inputs provided by Germany’s National Meteorological Service (Deutscher Wetterdienst – DWD). The seasonal climate forecasts are based on a large ensemble of daily predictions available up to 6 months in the future. These forecasts are downscaled to a spatial resolution of 6 km. The following analysis is based on aggregation of these daily values to monthly and seasonal values. New Hydrological Insights for the RegionDownscaled historical forecasts (hindcasts) and historical discharge observations over the last 15 years are used to assess the skill of the seasonal forecast in several Alpine basins. In general, the results show a limited skill. Therefore, the seasonal discharge forecasting system in this region is still not sufficient to incorporate the results into water resources decision support systems.

Hymec: Surface Precipitation Type Estimation at the German Weather Service

AbstractThe analysis and forecast of precipitation characteristics is a key task for national meteorological services for providing high-quality weather forecasts and warnings. Beside the precipitation amount, the precipitation type is essential to describe and evaluate the recent, ongoing, and future weather situations. This paper introduces a new surface-based hybrid hydrometeor classification algorithm. The presented method combines polarimetric radar observations at radar beam height from the C-band dual-polarization weather radar network of the German Weather Service [Deutscher Wetterdienst (DWD)] with corrected thermodynamical profiles of numerical weather prediction (NWP) model output and extrapolates the hydrometeor classes at radar beam height to a height of 2 m above ground level (AGL). The implemented technique parameterizes the microphysical processes in the lower troposphere based on the appropriate thermodynamical profile that the hydrometeors have to pass along their way from the radar beam height to the surface. Due to errors in NWP output, the NWP vertical profiles of temperature and humidity are adjusted by using several types of surface stations with high spatial and temporal resolution. Verification results show considerable improvements in the hydrometeor classification near the ground compared to the radar sweep height. After an additional positive in-house evaluation, the presented method is integrated into DWD’s operational environment. The topic of this paper is to describe the processing steps for the computation of the near-surface precipitation type. In addition, example cases and a verification study complement the explanations.

Calibrated ensemble forecasts of the height of new snow using quantile regression forests and ensemble model output statistics

Abstract. Height of new snow (HN) forecasts help to prevent critical failures of infrastructures in mountain areas, e.g. transport networks and ski resorts. The French national meteorological service, Météo-France, operates a probabilistic forecasting system based on ensemble meteorological forecasts and a detailed snowpack model to provide ensembles of HN forecasts. These forecasts are, however, biased and underdispersed. As for many weather variables, post-processing methods can be used to alleviate these drawbacks and obtain meaningful 1 to 4 d HN forecasts. In this paper, we compare the skill of two post-processing methods. The first approach is an ensemble model output statistics (EMOS) method, which can be described as a nonhomogeneous regression with a censored shifted Gamma distribution. The second approach is based on quantile regression forests, using different meteorological and snow predictors. Both approaches are evaluated using a 22 year reforecast. Thanks to a larger number of predictors, the quantile regression forest is shown to be a powerful alternative to EMOS for the post-processing of HN ensemble forecasts. The gain of performance is large in all situations but is particularly marked when raw forecasts completely miss the snow event. This type of situation happens when the rain–snow transition elevation is overestimated by the raw forecasts (rain instead of snow in the raw forecasts) or when there is no precipitation in the forecast. In that case, quantile regression forests improve the predictions using the other weather predictors (wind, temperature, and specific humidity).

Numerical weather prediction and climate modelling: Challenges and opportunities for improving climate services delivery in Southern Africa

Numerical weather predictions (NWP) and climate modelling activities underpin the generation of climate services to different stakeholders. In this study we assess the capabilities of National Hydrological and Meteorological Services (NMHSs) of the SADC region in performing NWP and climate modelling activities in support of generation of science-based climate information to the ever-growing number of stakeholders in the region. We first administered an online questionnaire to 15 SADC NMHSs gauging their status in terms of modelling capabilities. The online questionnaire was then followed by site visits to verify the online responses. Finally, we convened a validation workshop with representatives from all visited NMHSs. The results from the survey revealed a great disparity in the region in terms of capacity for performing NWP and climate modelling activities. Only 60% of the NMHSs in the region are either running or testing NWP models. Climate modelling activities are relegated to the second plan as only about 20% of NMHSs are dynamically downscaling seasonal forecasts and generating climate change projections. The challenges impeding running NWP and climate models in the SADC NMHSs are many and include lack of appropriate infrastructure (hardware and software), inadequate meteorological telecommunications and communication systems for rapid data exchange, lack of qualified staff, and insufficient budget to cater for acquisition and maintenance of meteorological equipment. More investment in the Meteorological sector is needed for NMHSs in the SADC region to provide timely and accurate climate information.

The implications of climate change for Veterinary Services.

Climate change due to increasing greenhouse gas (GHG) emissions is one of the most pressing issues facing society on a global scale. The growth of GHG emissions between 2000 and 2010 was higher than in each of the previous three decades, and each of the past four decades has been successively warmer than any preceding decades since 1850. Continued GHG emissions will cause further warming and changes in the climate system. Climate change affects livestock production in multiple ways, both directly and indirectly. Many of the impacts on the livestock sector result from increasing frequency and magnitude of weather and climate extremes such as droughts, flash floods, untimely rains, frost, hail and severe storms. This article describes some of the most vulnerable disaster communities in Asia, Africa, Australia, Europe and South America. It then describes the importance of meteorological information provided by national Meteorological and Hydrological Services to help Veterinary Services support sustainable management of livestock in vulnerable pastoral communities.

Climate services' role in safeguarding pastoral disaster communities.

Climate change due to increasing greenhouse gas (GHG) emissions is one of the most pressing issues facing society on a global scale. The growth of GHG emissions between 2000 and 2010 was higher than in each of the previous three decades, and each of the past four decades has been successively warmer than any preceding decades since 1850. Continued GHG emissions will cause further warming and changes in the climate system. Climate change affects livestock production in multiple ways, both directly and indirectly. Many of the impacts on the livestock sector result from increasing frequency and magnitude of weather and climate extremes such as droughts, flash floods, untimely rains, frost, hail and severe storms. This article describes some of the most vulnerable disaster communities in Asia, Africa, Australia, Europe and South America. It then describes the importance of meteorological information provided by national Meteorological and Hydrological Services to help Veterinary Services support sustainable management of livestock in vulnerable pastoral communities.

Evaluation of synthetic satellite images computed from radiative transfer models over a region of South America using WRF and GOES‐13/16 observations

AbstractSynthetic infrared GOES‐13 observations are generated from a high‐resolution (4 km) Weather Research and Forecasting (WRF) model run over Argentina for a meteorological event of deep moist convection using two different radiative transfer models. The fast operational Community Radiative Transfer Model (CRTM) and the physics‐based research model Atmospheric Radiative Transfer Simulator (ARTS) are compared with the available observations. CRTM shows good results at a low computational cost, and is a good candidate for operational use in the region. CRTM and ARTS synthetic satellite images show differences due to the treatment of the bulk scattering properties of the frozen hydrometeor species, especially around the cloud shield. With this WRF+CRTM configuration, a long‐term evaluation is conducted over 12 hr forecasts from one month of data in a region of the South American autumn with four different initializations at 0000, 0600, 1200 and 1800 UTC, as operational at the National Meteorological Service of Argentina. The simulated and observed GOES‐16 Advanced Baseline Imager (ABI) brightness temperatures (BTs) in the 6.9 m channel show good agreement with an averaged bias of K (RMSE 21.7 K), whereas the performances in the IR window channels show slightly larger averaged BT differences of 3.5 K (RMSE 15.2 K). BT differences between the 6.9 and 10.3 m channels indicate that the WRF+CRTM simulations underestimate low to mid‐level clouds and to a lesser extent high‐level clouds. In the BT (10.3 m) range between approximately 215 and 230 K, there is an overestimation of clouds with BT differences above 0 K between 6.9 and 10.3 m. This could be due to the misrepresentation of upper‐level clouds.

Validación de los pronósticos de precipitación simulados con el modelo BRAMS para la región La Libertad-Perú

The objective of this work is to validate the precipitation forecasts simulated with the Brazilian Regional Atmospheric Modelling Systems (BRAMS) numerical model in its version 5.3, for the La Libertad Region in Peru; this region of Peru has 80% of its land located in the northern highlands of Peru. In mountainous areas, precipitation is strongly influenced by the height of the terrain, a poor representation of the topographic elevations and depressions of the terrain can lead to an erroneous representation of the resolvable phenomena explicit by the model. Taking into account this characteristic of the study area, in the first instance the BRAMS model was configured to adequately represent the rugged orography of the local área at a horizontal resolution of 10 km. Then, the model was run to simulate precipitation forecasts at time horizons of 24, 48, 72, 96 and 120 hours, for the months of December 2019, January and February 2020. The validation of the forecasts was performed against observed data obtained from the National Meteorological and Hydrological Service (SENAMHI) of Peru, using quality indices for continuous and binary variables. From the results obtained, it is concluded that the BRAMS model performed well in forecasting the occurrence of precipitation for all time horizons. However, the model had difficulties in forecasting the occurrence of precipitation for higher thresholds, and predicted more false alarms for these thresholds. Finally, the model applied to the La Libertad Region in Peru, with a fairly rugged topography, had similar results to those obtained by other regional models applied in areas where there is little influence of terrain height.

Elaboration of Octagonal Roses to represent the wind patterns in the Port de Veracruz during the last 10 years

The Port de Veracruz and its surroundings are affected by meteorological phenomena such as the passage of frontal systems, among others. The main objective of this writing is to carry out continuous monitoring to categorize wind behavior, in order to determine patterns in the last 10 years. Therefore, climatological records of the intensity and direction of the average wind speeds are presented; wind data from three Automatic Meteorological Surface Stations (EMAS) of the Meteorological Observation Network of the Secretariat of the Navy, one of the National Meteorological Service (SMN) and two buoys of the National Oceanic and Atmospheric Administration (NOAA) were processed and analyzed. The results indicated that the North (N) direction predominately governs during the cold front season, while the East (E) winds predominately govern during the warm months (June, July, August and September). The maximum gusts, which can be an important factor in accidents, especially when their intensity exceeds 20 knots, affect various human, maritime, agricultural and livestock activities and tourism.

Do weather parameters affect emergency room visits due to acute urinary retention?

Objective: This study aimed to describe the effects of the various climate parameters on emergency room (ER) visits due to acute urinary retention (AUR). Patients and methods: This was a single-center retrospective analysis of the ER data of visits due to AUR in males and females between 2010 and 2017. We incorporated ER registries with data from the national meteorological service. Using multivariable logistic regression analyses, we assessed associations between climate parameters and the incidence of AUR. Results: A total of 1917 patients were admitted to the ER due to AUR during the study period, of whom 1706 (89%) were males and211 (11%) were females. Most AURs occurred in patients >70 years of age, with males on average being older than women (72.8 vs. 70.1 years old, p=0.01). Multivariable logistic regression analyses demonstrated that females had more AURs than men (odds ratio (OR)=1.3, 95% confidence interval (CI) 1.1–1.9, p=0.04) in spring. In all seasons except winter, a higher maximal temperature was associated with an increased risk of AUR, while in winter, a lower temperature was associated with an increased risk (OR=0.654, 95% CI 0.602–0.711, p<0.0001). Additionally, the following were associated with an increased AUR risk: increased wind speed and lower heat index during the spring, increased precipitation and a lower heat index during the autumn, and a higher heat index in the winter. Conclusions: Our data suggest that weather parameters might be associated with an increased AUR incidence in a Mediterranean coastline area, with maximal ambient temperature, wind speed, heat index, and precipitation playing a potential role. Level of evidence: 6.

Causal graph analysis of COVID-19 observational data in German districts reveals effects of determining factors on reported case numbers.

Several determinants are suspected to be causal drivers for new cases of COVID-19 infection. Correcting for possible confounders, we estimated the effects of the most prominent determining factors on reported case numbers. To this end, we used a directed acyclic graph (DAG) as a graphical representation of the hypothesized causal effects of the determinants on new reported cases of COVID-19. Based on this, we computed valid adjustment sets of the possible confounding factors. We collected data for Germany from publicly available sources (e.g. Robert Koch Institute, Germany's National Meteorological Service, Google) for 401 German districts over the period of 15 February to 8 July 2020, and estimated total causal effects based on our DAG analysis by negative binomial regression. Our analysis revealed favorable effects of increasing temperature, increased public mobility for essential shopping (grocery and pharmacy) or within residential areas, and awareness measured by COVID-19 burden, all of them reducing the outcome of newly reported COVID-19 cases. Conversely, we saw adverse effects leading to an increase in new COVID-19 cases for public mobility in retail and recreational areas or workplaces, awareness measured by searches for "corona" in Google, higher rainfall, and some socio-demographic factors. Non-pharmaceutical interventions were found to be effective in reducing case numbers. This comprehensive causal graph analysis of a variety of determinants affecting COVID-19 progression gives strong evidence for the driving forces of mobility, public awareness, and temperature, whose implications need to be taken into account for future decisions regarding pandemic management.