National Meteorological Service Research Articles

Evaluation of Gridded Rainfall Products in Three West African Basins

In recent years, accessing rainfall data from ground observation networks maintained by national meteorological services in West Africa has become increasingly challenging. This is primarily due to high acquisition costs and the often sparse distribution of rainfall gauges across the region, which limits their use in hydrological studies and related research. At the same time, the rising availability of precipitation products derived from satellite/earth observations, reanalysis datasets, and in situ measurements presents exciting prospects for hydrological applications. Nonetheless, these datasets constitute indirect measurements, necessitating rigorous validation against ground-based rainfall data. This study comprehensively assesses twenty-three gridded rainfall products, including sixteen from satellites, six from reanalysis data, and one from in situ measurements, across the Senegal, Gambia, and Casamance River basins. Performance evaluation is conducted across distinct climatic zones, both pre- and post-resampling against observed rainfall data gathered from forty-nine rainfall stations over a six-year period (2003–2008). Evaluation criteria include the Kling–Gupta Efficiency (KGE) and Percentage of Bias (PBIAS) metrics, assessed at daily, monthly, and seasonal time steps. The results reveal distinct performance levels among the evaluated rainfall products. RFE, ARC2, and CPC notably yield the highest KGE scores at the daily time step, while GPCP, CHIRP, CHIRPS, RFE, MSWEP, ARC2, CPC, TAMSAT, and CMORPHCRT demonstrate superior performance at the monthly time step. During the rainy season, these products generally exhibit robustness. However, rainfall estimates derived from reanalysis datasets (ERA5, EWEMBI, MERRA2, PGF, WFDEICRU, and WFDEIGPCC) perform poorly in the studied basins. Based on the PBIAS metric, most products tend to underestimate precipitation values, while only PERSIANN and PERSIANNCCS lead to significant overestimations. Spatially, optimal performance of the products is observed in the Casamance basin and the Sudanian and Sahelian climatic zones within the Gambia and Senegal basins. Conversely, in the Guinean zone of the Gambia and Senegal Rivers, the rainfall products displayed the poorest performance.

Parallel prediction of dengue cases with different risks in Mexico using an artificial neural network model considering meteorological data.

In 2022, Mexico registered an increase in dengue cases compared to the previous year. On the other hand, the amount of precipitation reported annually was slightly less than the previous year. Similarly, the minimum-mean-maximum temperatures recorded annually were below the previous year. In the literature, it is possible to find studies focused on the spread of dengue only for some specific regions of Mexico. However, given the increase in the number of cases during 2022 in regions not considered by previously published works, this study covers cases reported in all states of the country. On the other hand, determining a relationship between the dynamics of dengue cases and climatic factors through a computational model can provide relevant information on the transmission of the virus. A multiple-learning computational approach was developed to simulate the number of the different risks of dengue cases according to the classification reported per epidemiological week by considering climatic factors in Mexico. For the development of the model, the data were obtained from the reports published in the Epidemiological Panorama of Dengue in Mexico and in the National Meteorological Service. The classification of non-severe dengue, dengue with warning signs, and severe dengue were modeled in parallel through an artificial neural network model. Five variables were considered to train the model: the monthly average of the minimum, mean, and maximum temperatures, the precipitation, and the number of the epidemiological week. The selection of variables in this work is focused on the spread of the different risks of dengue once the mosquito begins transmitting the virus. Therefore, temperature and precipitation were chosen as climatic factors due to the close relationship between the density of adult mosquitoes and the incidence of the disease. The Levenberg-Marquardt algorithm was applied to fit the coefficients during the learning process. In the results, the ANN model simulated the classification of the different risks of dengue with the following precisions (R2): 0.9684, 0.9721, and 0.8001 for non-severe dengue, with alarm signs and severe, respectively. Applying a correlation matrix and a sensitivity analysis of the ANN model coefficients, both the average minimum temperature and precipitation were relevant to predict the number of dengue cases. Finally, the information discovered in this work can support the decision-making of the Ministry of Health to avoid a syndemic between the increase in dengue cases and other seasonal diseases.

Climatological reference stations: Definitions and requirements

AbstractGround‐based stations are one of many observing systems contributing to the generation of data to evaluate climate trends and variations locally and globally. Networks of stations are made of various numbers of observing sites, equipped with different typologies and varieties of instruments, differently managed and maintained. Among such networks a limited number of stations are required as a reference, to provide top quality traceable measurements and as the top level of a tiered approach; it is these stations which are here designated as Climatological Reference Stations (CRS). At present, there is no agreed definition of the key instrumental and technical features of a CRS, nor are there defined reference measurement procedures. This leads to the situation of a multitude of approaches among different National Meteorological and Hydrological Services (NMHSs), research institutes and other agencies that reduces the comparability of results in space and in time. The lack of CRSs, moreover, is a major contributor to the huge efforts required to harmonize data and detect biases locally, regionally and globally, as in their absence there is a need to fall back on other stations which are more likely to have such biases. This article reports on the outcomes of the work of a group of experts, nominated by the World Meteorological Organization (WMO) Commission for Climatology (CCI), tasked to promote a standardized and agreed concept about the definitions, specifications and technical characteristics of climate reference data and stations for ground‐based networks, with the addition of a practical example of climatological reference station installed in the framework of an European funded project, with the purpose of comparing and confirming the prescribed characteristics and guidance, with respect to practical field use and available instrumentation. The adoption of a unique definition and prescription on the technical setup, measurement procedures and uncertainty evaluation will substantially progress towards a common approach in detecting climate trends. This will improve data comparability in space and time and allow a more robust understanding of climate evolution locally and globally.

Impact of Temperature and Atmospheric Pressure on Hospitalizations of Patients Presenting With Acute Coronary Syndrome.

This study aims to investigatethe impact of temperature and atmospheric pressure on hospitalizations of patients with acute coronary syndrome (ACS). This is a retrospective, observational, analytical study conducted in a single center, University Hospital Center "Mother Teresa," Tirana, Albania, in the period January-December 2018. This study included 1,165 patients with ACS, who performed urgent coronary angiography, from January 2018 to December 2018. Patients were diagnosed withACS based on clinical and examination findings. The data were collected retrospectively using patient files. Baseline demographic, clinical, and procedural characteristics were collected. Data on atmospheric parameters, measured at the weather monitoring station, were obtained from the National Meteorological Service database. Measurements from the meteorological service provided values ​​for each parameter: average daily temperature and atmospheric pressure in each country district. Atmospheric data measurements were taken for the day under review. The number of inhabitants for the respective districts is taken from the National Institute of Statistics (INSTAT). The study involved 1,165 patients, with a mean age of 63.1 years, ranging from 27 years to 89 years old. The majority of patients (78.6%) were male, while 21.4% were female. A statistically significant relationship was observed between seasonal changes in temperature and atmospheric pressure concerning the number of cases with ACS; the autumn season prevails with 27.9% of the total cases, followed by the spring season with 25.6%, the summer season with 24.2%, and winter season with 22.3% (p = 0.04). Additionally, significant changes in the average monthly values ​​of temperature and atmospheric pressure were accompanied by a statistically significant increase in the number of cases as occurred in March-April and October-November (p ≤ 0.05). Most cases in the cold period (November-March) occurred on days with a change in temperature or atmospheric pressure with a statistically significant value of p < 0.05. An important relationship between seasonal, monthly, and daily changes in temperature and atmospheric pressure concerning the frequency of cases with ACS was observed.

The importance of crowdsourced observations for urban climate services

AbstractCrowdsourced observation networks are typically much more dense than those maintained by National Meteorological Services, and sample a much wider range of local climates. This offers an opportunity to build observed climatologies that are more representative of lived experience, particularly in cities. This study provides a worked example to show their potential for improving operational climate services, and to identify the challenges to realizing that potential. To demonstrate the concept, data from personal weather stations, obtained through citizen science, are used to build an observed record of daily maximum temperatures in 2020 in Manchester (UK). This record is compared to the standard baseline used in a current climate service, showing a substantial increase in the estimated heat hazard. If such potential benefits are to be realized in a climate service, it will be necessary to first build an alternative observed baseline of decadal length and at national or international scale. This requires further work to acquire, quality‐control, exposure‐control and map the crowdsourced observations.

A charge emitter for use in evaluating aircraft rainfall enhancement

Charging of droplets is known to influence their properties and interactions, such as through modifying their evaporation, collisions or stability. Introducing additional charge into a droplet system, such as a natural fog or cloud may therefore provide a route to modify its behaviour. As aircraft are already used in rainfall enhancement activities by some national meteorological services, adapting their existing delivery systems to include charge release provides a convenient method with which to pursue this. Rainfall enhancement aircraft typically carry an array of under-wing tubes into which cloud seeding agents (e.g. salt or silver iodide) are contained in flares which are loaded pre-flight. Our approach for a charge release system is to construct a stand-alone device able to fit within an existing flare housing, to provide controlled release of ions by corona emission. The “flare emitter” device is battery powered and able to release either polarity of corona ions. It contains a programmable microcontroller (Arduino Nano) able to operate in a fixed sequence and record data to an internal memory card at 10Hz. Monitoring systems for the corona emission voltage and current are included, as well as environmental sensors for pressure, temperature and relative humidity.

Assessment of Climate Trend, Meteorological Drought and Farmers' Perception to Climate Change and Variability in Hadero Tunto District, Southern Ethiopia

Currently, climate change and variability are a hot issue, and more attention needs to be paid to their impact on Ethiopia. Farmers' perception plays a significant role in successfully implementing of adaptation strategies to reduce climate change impacts. The main objective of this study was to assess climate trends, meteorological drought and farmers' perception of climate change variability in Hadero Tunto district, Kembata Tembaro Zone, Southern Ethiopia. To determine historical climate trends, 30 years of rainfall and temperature data were obtained from the Hawassa branch of the Ethiopian National Meteorological Service, and trend analysis was performed. The multistage sampling technique was used to select 150 farm households to be surveyed. The modified Mann–Kendall and Sens' slope estimator trend tests were applied to detect the statistical significance of the trend as well as the magnitude in the time series data. The standardized precipitation index was computed for short and long-term scales to characterize the moisture content in the study area. Results of the standardized precipitation index revealed that the total number of drought events was higher in spring than summer. Still, the most extreme drought was recorded in summer. The years 1987 and 2015 experienced extreme drought in summer with indices values of -2.89 and -2.03, respectively, while, -2.27 was the spring season of 2003. In the long-term scale, -1.7 was recorded as the severe value. The historical average annual maximum and minimum temperatures both showed a significant upward trend. Historical annual rainfall shows a negligible downward trend from 1987 to 2016. This trend of rising temperatures and changing rainfall threatens smallholders´ agriculture, who are already limited by access to basic equipment and land use challenges. The descriptive result revealed that most people perceived long-term variability in the rainfall amount and distribution pattern and an increasing trend and variability of temperature. Based on these results, the study recommends that agricultural extension services be enhanced to sensitize the farmers about climate change, thus improving their perception. Further, studies at a larger scale to illustrate the associations between farmers' perceptions of climate change with meteorological data to figure out the risk of climate change are also critical

Influência de um corredor fluvial na variação de elementos climáticos: O caso da Cidade de Neuquén (Argentina)

This paper aimed to compare the behavior of climatic elements and extreme thermal events between two landscape units of the city of Neuquén, Argentina, for the period 2013-2020. The study involved daily data of minimum temperature (Tmin), maximum temperature (Tmax), and relative humidity (RH) obtained from the meteorological stations "Neuquén Aero" (NA) and "Paseo de la Costa" (PC), referring to the valley (peri-urban area) and the fluvial corridor (riparian environment) respectively. The influence of the riverbank on the daily Tmin and Tmax during Heat Waves (HW) and Cold Waves (CW) in the valley was analyzed using the methodology of the National Meteorological Service. The mean Tmax (Tmin), at annual, seasonal, and monthly resolutions, were lower (higher) in PC. The mean RH values were higher in NA. During the HW, a regulatory effect was exhibited regarding the number of days that the threshold values were exceeded consecutively in the fluvial littoral.

Heat-related mortality in Frankfurt am Main, Germany, from 2000 to 2023.

The major heatwave in Europe in August 2003 resulted in 70,000 excess deaths. In Frankfurt am Main, a city with 767,000 inhabitants in the south-west of Germany, around 200 more people died in August 2003 than expected. Soon afterwards, the city introduced adaptation measures to prevent heat-related health problems and subsequently established further mitigation measures to limit climate change. Frankfurt is rated as being one of the cities in Germany to have implemented the best climate adaptation and mitigation measures. This study addressed the following questions: is there already a downward trend in mortality from heat and can this be attributed to the measures taken? The age-standardized mortality rate (ASR) was calculated for the months of June to August and for calendar weeks 23 to 34 of the individual years on the basis of population data and deaths of the inhabitants of Frankfurt am Main for the years 2000 to 2023. This was related to the meteorological data from the Frankfurt measuring station of the German National Meteorological Service. For four different heat exposure indicators (heat days, days in heat weeks, days in heatwaves and days with heat warnings), the incidence rate (death cases per 1 million person days) (IR) was calculated for days with and without exposure, and the incidence rate difference and the incidence rate ratio (IRR) were estimated to compare days with vs days without exposure. Over the years, the mean daily temperatures tended to increase, and the standardized mortality rate decreased. An increase in ASR was observed during heatwaves up to 2015, but no longer in the later ones. In the summer of 2003, the incidence rate was 16.0 (95% confidence interval (CI) 12.2-19.9) per 1 million person days greater on heat days than on days not classified as heat days, and the corresponding incidence rate ratio was 1.64 (95% CI 1.48-1.82). Although the weather data for the summers of 2018 and 2022 were comparable with the record-breaking heat summer of 2003, the incidence rate differences (2018: 3.8, 95% CI 0.9-6.7; 2022: 2.3, 95% CI -0.3-4.9) and the IRR (2018: 1.20, 95% CI 1.05-1.37; 2022: 1.12, 95% CI 0.99-1.26) were considerably lower. Similar results were also obtained when comparing mortality in heat weeks and heatwaves as well as on days with heat warnings. In summary, our study in Frankfurt am Main not only showed a decrease in heat-related mortality in the population as a whole over the years, but also a decrease in excess mortality during various heat periods (day, week, wave, warning), especially in comparison with the years with very high heat stress and drought (2003, 2018 and 2022). However, whether this development represents success of the intensive prevention measures that have been implemented in the city for years or merely describes a general trend cannot be answered with certainty by the present study. To answer this question, a comparative study should be carried out in various municipalities in the Rhine-Main region with different levels of intensity in dealing with the heat problem.

Intercomparing the quality of recent reanalyses for offshore wind farm planning in Germany's exclusive economic zone of the North Sea

Abstract. In order to facilitate offshore wind farm tenders, Deutscher Wetterdienst (DWD, Germany's national meteorological service) provides reanalysis data and quality assessments to Bundesamt für Seeschifffahrt und Hydrographie (BSH, Federal Maritime and Hydrographic Agency). The regional reanalysis COSMO-REA6 is used besides the global reanalysis ERA5. New reanalyses and derived products getting available are (i) the regional reanalysis CERRA (C3S), (ii) COSMO-R6G2, a successor of COSMO-REA6 which is currently produced by DWD and (iii) HoKliSim-De, a convection-permitting climate simulation for Germany with COSMO-CLM as a regional downscaling of ERA5. In the present study, the quality of the different data sets for offshore wind energy application is compared using in-situ measurements of the wind speed and wind direction from the top anemometer and vane of the FINO1 research platform and satellite-based data of the near-surface wind speed from the Copernicus Marine Environment Monitoring Service (CMEMS) and the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF). Evaluation at FINO1 focuses on the time period prior to the installation of nearby wind farms to avoid wake effects. COSMO-REA6, CERRA and HoKliSim-De show only small biases and resemble the observed distribution of the wind speed at FINO1 whereas ERA5 shows slightly lower values of the wind speed at 100 m. All model-based products tend to slightly underestimate the occurrence of south-westerly wind directions and overestimate wind directions from West to Northwest. Smallest directional biases are analysed for COSMO-REA6. Analysis of the windstorm CHRISTIAN suggests that ensemble information is required for the representation of individual extreme events. Evaluation of the near-surface wind speed using satellite-based data is performed for an area around the German Exclusive Economic Zone (EEZ) of the North Sea. The median bias of ERA5 and COSMO-REA6 is close to zero. CERRA shows a systematic overestimation of the near-surface wind speed compared to the satellite-based reference datasets. By contrast, a slight underestimation is analysed for HoKliSim-De. The bias distribution analysed for a first simulation stream of COSMO-R6G2 is similar to COSMO-REA6 which provides initial indication for the applicability of the new product.

A stratospheric mission – design of a conceptual framework to bring weather balloons and STEM into the classroom

IntroductionIntegrated STEM education makes learning relevant and applicable, blending the mindset, skillset, and toolset necessary in developing a depth of understanding for science, technology, engineering and mathematics.MethodsBased on the theoretical framework of Kelley and Knowles the ‘Spaceship Earth’ project was designed to engage primary school children in 4th class (ages 9 and 10 years old) in real-world project-based experiments in the classroom that are used to develop collaborative problem-solving skills and a framework for asking and answering scientific questions. This project involved researchers from two Universities and the Irish National Meteorological Service working collaboratively on a high-altitude balloon mission.ResultsHigh-altitude balloons have an established track record of safe and effective use in weather forecasting, astronomy, and STEM outreach. During the project lifecycle the children devised experiments that they launched to the stratosphere using the high-altitude balloons. Once the experiments returned to Earth, the children engaged in analysis and discussion about their experiments that extended and deepened their learning.DiscussionThis celestial project framework represents a new dawn of innovation for STEM education and public engagement.

Ensemble Forecast Sensitivity to Observations Impact (EFSOI) applied to a regional data assimilation system over south-eastern South America

Observations that are assimilated into numerical weather prediction systems are conformed by numerous data sets and their impact should be objectively evaluated. This can be efficiently achieved by the Forecast Sensitivity to Observation Impact (FSOI) methodology. In this study we explore the application of the ensemble formulation of FSOI (EFSOI) in a regional data assimilation system over south-eastern South America and evaluate the observations that result beneficial or detrimental to the forecast. In this paper we focus on the impact of two types of surface weather stations: human-operated conventional surface weather station networks from the National Meteorological Service and non-conventional automatic surface weather stations from different public and private networks (CSWS and NSWS respectively). To achieve this, the Weather Research and Forecasting model coupled with the Local Ensemble Transform Kalman Filter is used with 20 members. The experiment was carried out during 30 days of the intensive observing period of the RELAMPAGO-CACTI (Remote sensing of Electrification, Lightning, And Mesoscale/microscale Processes with Adaptive Ground Observations-Clouds, Aerosols, and Complex Terrain Interaction) field campaign that was conducted during the 2018–2019 austral warm season in the center of Argentina. 20 km resolution analyses were obtained every 6-h, assimilating data from soundings, aircrafts, GOES derived motion winds, AIRS retrievals, CSWS and NSWS. It is shown that, considering the entire period, all the observation sources had a positive impact on the 6-h forecasts. However, when each variable is analyzed individually, surface pressure observations from both CSWS and NSWS show on average a negative impact. Two data denial experiments were carried out in order to support EFSOI results. This paper is the first approximation to quantify the impact of different observation sources and observed variables at a regional scale over south-eastern South America, a data sparse region where unconventional networks can help close large geographical data gaps. The results of this work help identify observation data sources detrimental for the data assimilation system, suggesting data selection criteria to improve the regional forecast accuracy.

WRF Parameterizations of Short-Term Solar Radiation Forecasts for Cold Fronts in Central and Eastern Europe

The solar power industry is a rapidly growing sector of renewable energy, and it is crucial that the available energy is accurately forecast. Using numerical weather prediction models, we can forecast the global horizontal irradiance on which the amount of energy produced by photovoltaic systems depends. This study presents the forecast effects for one of the most challenging weather conditions in modelling, occurring in central and eastern Europe. The dates of the synoptic situations were selected from 2021 and 2022. Simulations were carried out for 18 days with a cold front and, in order to verify the model configuration, for 2 days with a warm front, 2 days with an occlusion front and 2 days with a high pressure situation. Overall, 24 forecasts were made for each of the three parameterizations of the Weather Research and Forecasting model. The data were compared with the values measured in situ at the station performing the actinometric measurements belonging to Germany’s National Meteorological Service. This paper presents the spatial distribution of the global horizontal irradiance parameters for several terms to explain the differences between the results of the different simulations.

Towards nowcasting in Europe in 2030

AbstractThe increasing impact of severe weather over Europe on lives and weather‐sensitive economies can be mitigated by accurate 0–6 h forecasts (nowcasts), supporting a vital ‘last line of defence’ for civil protection and many other applications. Recognizing lack of skill in some complex situations, often at convective and local sub‐kilometre scales and associated with rare events, we identify seven recommendations with the aim to improve nowcasting in Europe by the national meteorological and hydrological services (NMHSs) by 2030. These recommendations are based on a review of user needs, the state of the observing system, techniques based on observations and high‐resolution numerical weather models, as well as tools, data and infrastructure supporting the nowcasting community in Europe. Denser and more accurate observations are necessary particularly in the boundary layer to better characterize the ingredients of severe storms. A key driver for improvement is next‐generation European satellite data becoming available as of 2023. Seamless ensemble prediction methods to produce enhanced weather forecasts with 0–24 h lead times and probabilistic products require further development. Such products need to be understood and interpreted by skilled forecasters operating in an evolving forecasting context. We argue that stronger co‐development and collaboration between providers and users of nowcasting‐relevant data and information are key ingredients for progress. We recommend establishing pan‐European nowcasting consortia, better exchange of data, common development platforms and common verification approaches as key elements for progressing nowcasting in Europe in this decade.

The effect of components capacity loss on the performance of a hybrid PV/wind/battery/hydrogen stand-alone energy system

A stand-alone hybrid renewable energy system (HRES) comprising a photovoltaic (PV) array and a small wind turbine for energy production, a lead-acid battery for short-term energy storage, and a hydrogen subsystem for long-term energy storage is analyzed in this study. The hydrogen subsystem comprises a PEM electrolyzer for hydrogen production, a hydrogen compressor and a hydrogen tank for hydrogen storage, and a PEM fuel cell for power generation. Twenty-year hourly input weather data were acquired from the national meteorological service for the Mediterranean location of Split, Croatia. Two different end-user load profiles are considered, namely a year-around constant load profile and the stochastically variable load profile. Furthermore, a comparative analysis is performed for two different energy management strategies – one based on energy balance within the system, and the other based on double hysteresis loop control. The HRES performance is simulated using MATLAB. The dynamics of capacity loss of the batteries, PEM electrolyzer, and PEM fuel cell is a function of how the system operates, but at the same time, this dynamics of capacity loss affects the overall system performance. The double hysteresis control strategy results in a significantly longer battery lifetime for both load profiles considered, while the PV and battery loss of capacity causes more frequent electrolyzer and fuel cell operation for all considered cases.

Exploratory analysis of citizen observations of hourly precipitation over Scandinavia

Abstract. We present a comparison between Netatmo hourly precipitation amounts and observations of the same quantity from weather stations managed by national meteorological services, the latter used as reference values. The empirical distributions of the crowdsourced observations in the surroundings of reference stations are used to assess accuracy and precision of crowdsourced data. We found that reference values are typically within the distribution of the crowdsourced data. However, as the amount of precipitation increases, the spread of the crowdsourced distribution increases and the reference values are more and more frequently found towards the right tail of the distribution. These results indicate that accuracy and precision of crowdsourced data change as precipitation increases. We have studied the sensitivity of our results to the size of the neighbourhood chosen around the reference stations and we show that by aggregating the values over those neighbourhoods, crowdsourced data can be trusted in determining precipitation occurrence. We have assessed the variability of precipitation within small neighbourhoods (of radius 1, 3 and 5 km) and we provide estimates on the basis of the precipitation amounts. Our study quantifies the variability of hourly precipitation over small regions, of the size of the so-called “unresolved spatial scales” in limited area models, based on three years of data collected at several places in Scandinavia.

Methodological estimation to quantify drought intensity based on the NDDI index with Landsat 8 multispectral images in the central zone of the Gulf of Mexico

Introduction: Drought is a slow evolution phenomenon drastically affecting the environment and human activities. Nowadays, there are several indices to study drought. They can be based on in-site measurements of meteorological stations or remote perception data. However, Mexico’s number of functioning meteorological stations from the National Meteorological Service (NMS) is steadily decreasing. Nevertheless, the NMS reports drought conditions through the Mexican Drought Monitor (MDM), which uses different methods to estimate drought levels. These reports are provided every 15 days for each municipality. However, the methods the NMS utilizes are unknown to the general public. Thus, in-situ studies which try to estimate drought are limited by the MDM data constrictions. Consequently, remote perception is an alternative to solve the lack of stations and the MDM data restrictions, depending on the region. Therefore, this research aims to: 1) Develop a methodology to quantify drought intensity based on the Normalized Difference Drought Index (NDDI) with Landsat 8 multispectral images in the municipalities of the central zone of the Gulf of Mexico for drought and no drought periods. 2) Analyze and compare the NDDI behavior against the MDM from the NMS during the same periods.Methods: The methodology consisted of estimating the NDDI by using Landsat 8 multispectral images. Further on, NDDI drought values were compared with the MDM.Results: Results showed that NDDI values increase from July to October during a drought period, coinciding with months when precipitation is low, and temperature is high. Additionally, it was found that the NDDI coincides with the MDM data in 46% of the municipalities having drought conditions when temperatures increased 2.1°C and precipitations decreased by 668 mm. Furthermore, the NDDI coincided in 16% of the municipalities during no drought periods with the maximum increases in temperatures at 1.4°C and precipitation reduced by 386 mm.Discussion: The NDDI estimated by Landsat 8 images can determine drought behavior in the study zone during periods with limited reduced precipitation and temperature increases.

TRANSLATE: standardized climate projections for Ireland

The TRANSLATE project was established in 2021 by Met Éireann, the Irish national meteorological service, to provide standardized future climate projections for Ireland. This paper outlines the principles and main methods that were used to generate the first set of such projections and presents selected results to the end of the 21st century. Two separate ensembles of dynamically downscaled CMIP5 projections were analyzed. These produce very consistent results, increasing confidence in both, and in the methods used. Future projected fields show plenty of detail (depending on local geography), but the change maps relative to the base period are much smoother, reflecting the global climate change signal. Future forcing uncertainty is represented by 3 different emission scenarios, while model response uncertainty is represented by sub-ensembles corresponding to different climate sensitivities. The resulting matrix of distinct climate ensembles is complemented by ensembles of temperature threshold-based projections, drawn from the same underlying simulations.

Observed Changes in Rainfall and Characteristics of Extreme Events in Côte d’Ivoire (West Africa)

This study evaluates how the characteristics of daily rainfall and extreme events in Côte d’Ivoire changed during 1961–2015 using the rain gauge observation network of the National Meteorological Service (SODEXAM). The results indicate that the northern and southern parts of Cote d’Ivoire experienced a change from a wet to a dry period, with cut-offs in 1982 and 1983, respectively. In the northern part, this dry period was marked by a decrease in rainfall intensity, the length of wet spells, and the contribution of heavy and extreme rainfall, as well as an increase in the number of rainy days and a decrease in the length of dry spells. Over the southern part, this dry period was marked by an increase in the maximum length of dry spells associated with an increase in the maximum 1-day and 5-day precipitation events. The western part of Côte d’Ivoire experienced a late cut-off from the wet to dry period in 2000; the dry period was associated with a decrease in the number of rainy days, rainfall intensities, and maximum length of wet spells. Changes in the central part of Cote d’Ivoire presented high variability, and trends were less marked, even though a cut-off from a wet to dry period was detected in 1991. This study shows that Côte d’Ivoire, which is located in a subhumid and humid region and has an economy dependent on agriculture (especially cash crops, which comprise 60% of the GDP), is experiencing dry spells that are increasing in frequency and length. Combined with deforestation to increase production, this situation could lead to desertification and compromise the sustainable development goals of the country. The contribution of heavy rainfall was found to increase during the last 15 years, increasing the overall risk of floods, especially in urban areas where city authorities and populations are not prepared, thereby threatening infrastructure and human security.

The association between weather warnings and hip fractures in the Republic of Ireland

SummaryThis study examined the relationship between hip fractures and weather warnings with the hypothesis higher rates of fractures occur during warnings. National hip fracture database and weather warning records were utilised. Higher rates of hip fractures were found with severe warnings. This has implications for planning in future severe warnings.BackgroundHip fractures represent a significant burden on the Irish Health system with 3666 in 2020. The Irish National Meteorological Service operates a colour coded warning system. Yellow being least severe, while orange represents weather with capacity to impact individuals in affected areas and red represents advice to protect themselves and property. Previous studies investigated the seasonality of hip fractures, which remains but none have investigated the relationship between weather warnings and rates of hip fractures. The hypothesis was that increasing weather warnings would be associated with increased hip fractures. The aim was to investigate the relationship between weather warnings and hip fractures in the Republic of Ireland.MethodsComparison of national weather warning archives from 2013 to 2019 to Fracture Database records. Reviews assessed whether fractures occurred on days a weather warning was in place in the individual’s local county. A statistical analysis of warning features and their relationship to hip fractures was then performed. Fractures and weather warnings were stratified by county with both a panel and case crossover analysis performed.ResultsThere was a tendency towards increased incidence of hip fractures with weather warnings in adjusted analysis (IRR 1.02; 95%CI 0.99–1.06; p-value 0.123). Orange warnings were associated with a statistically higher incidence of fractures (IRR 1.06; 1.01–1.12; p-value 0.026). In both panel and case crossover analysis, both orange and yellow warnings were associated with fractures. Red warnings were associated with a lower incidence of fracture on day of warning (adjusted incidence rate ratio 0.92; 0.70–1.22; p-value 0.569) but a higher incidence on the following day (adjusted incidence rate ratio 1.14; 0.88–1.46; p-value 0.313).ConclusionAn increased incidence of hip fractures appears to occur during weather warnings. Consideration should be given when preparing for periods of extreme weather, ensuring sufficiently resources.