Meteorological Service Research Articles

Chronobiological perspectives and meteorological associations in symptomatic popliteal artery aneurysms

ABSTRACT The potential influence of circadian rhythm, seasonal variations, and alterations in meteorological parameters has been studied across various vascular events. However, there is a lack of evidence on the potential chronobiological impacts on thromboembolic events related to the most common peripheral aneurysm, the popliteal artery aneurysm (PAA). Data was obtained from a German PAA registry and the German Meteorological Service (Deutscher Wetterdienst). In this observational cohort study seasonality and chronobiology as well as associations with meteorological parameters of symptomatic PAA were investigated. In a multivariate logistic regression analysis, it was further analyzed whether meteorological parameters could distinguish asymptomatic from symptomatic patients in the registry. Of 1200 registered PAA, n = 142 PAA presented with acute limb ischemia between February 2011 and September 2022. More symptomatic patients (57.0%) presented to the hospital between January and June than in the second half of the year with a nadir in the fall season. Symptom onset was predominantly in the morning hours (39.5%). Atmospheric pressure and humidity values from the index dates diverged from a normal distribution showing a bimodal (“double-peak”) configuration. Most patients developed symptoms after a reduction in temperatures compared to 1 or 2 d prior to the index date. However, we found evidence for an interaction between age and temperature difference, where the effects of a decreasing temperature fade with increasing age. Facing the complexity of individual-environment interactions, further investigations are needed to determine whether meteorological parameters are true risk modifiers or surrogates for seasonal differences and altered behaviors.

Effect of Solar Irradiation Inter-Annual Variability on PV and CSP Power Plants Production Capacity: Portugal Case-Study

The sizing of solar energy power plants is usually made using typical meteorological years, which disregards the inter-annual variability of the solar resource. Nevertheless, such variability is crucial for the bankability of these projects because it impacts on the production goals set at the time of the supply agreement. For that reason, this study aims to fill the gap in the existing literature and analyse the impact that solar resource variability has on solar power plant production as applied to the case of Portugal (southern Europe). To that end, 17 years (2003–2019) of meteorological data from a network of 90 ground stations hosted by the Portuguese Meteorological Service is examined. Annual capacity factor regarding photovoltaic (PV) and concentrating solar power (CSP) plants is computed using the System Advisor Model, used here for solar power performance simulations. In terms of results, while a long-term trend for increase in annual irradiation is found for Global Horizontal Irradiance (GHI) and Direct Normal Irradiance (DNI), 0.4148 and 3.2711 kWh/m2/year, respectively, consistent with a solar brightening period, no corresponding trend is found for PV or CSP production. The latter is attributed to the long-term upward trend of 0.0231 °C/year in annual average ambient temperature, which contributes to PV and CSP efficiency reduction. Spatial analysis of inter-annual relative variability for GHI and DNI shows a reduction in variability from the north to the south of the country, as well as for the respective power plant productions. Particularly, for PV, inter-annual variability ranges between 2.45% and 12.07% in Faro and Santarém, respectively, while higher values are generally found for CSP, 3.71% in Faro and 16.04% in São Pedro de Moel. These results are a contribution to future instalments of PV and CSP systems in southern Portugal, a region with very favourable conditions for solar energy harvesting, due to the combination of high production capacity and low inter-annual variability.

Correspondence between Mawson and Simmers: the missing meteorological report of Ritchie Simmers from the BANZARE voyages of 1929–1931 in Antarctic waters

ABSTRACT The British, Australian and New Zealand Antarctic Research Expedition (BANZARE) operated during the 1929–1930 and 1930–1931 austral summers, primarily as a ship-borne expedition in the Southern Ocean. Involvement of meteorologist Ritchie Simmers from the New Zealand Meteorological Service was part of the New Zealand contribution to the expedition. He collected data assiduously, including two-hourly weather observations during the day and 34 balloon flights tracked by theodolite observations. Simmers worked up considerable data but did not produce anything in the BANZARE scientific report series, as expected by Professor Douglas Mawson, series editor and expedition leader. Based on correspondence in the Mawson Centre at the South Australian Museum between Mawson and Simmers about his pending report, we suggest reasons for the striking omission of meteorological information from the report series. Simmers had a busy, full-time career with the New Zealand Meteorological Service, and not until 1947 was he permitted to work on his report in office hours. He was distracted by other events, including studying at Massachusetts Institute of Technology (M.I.T.). We suggest Simmers’ enthusiasm for the project waned after Mawson rebuffed potential collaboration with Professor Rossby at M.I.T. involving BANZARE meteorological data. Little of Simmers’ data set has been located.

Educational management in personnel training of hydrometeorological services: world experience

Introduction. The relevance of the presented study is determined by the fact that the acceleration of climate change on the planet has a significant impact on almost all spheres of economic and social life of mankind, which imposes new requirements both on the information provided by hydrometeorological services and on the personnel of managers and specialists providing it.Purpose setting. The purpose of the presented studies is to summarize the world experience in the development of promising requirements for the skills and competence of managers and specialists of national meteorological services in the interests of forming a system of requirements for staffing and training of managers and specialists of hydrometeorological services of the Roshydromet system.Methodology and methods of the study. The methodological base of the research consists of well-known scientific works devoted to the promising requirements for the skills and competencies of managers and specialists of the hydrometeorological service. Results. The novelty of the obtained results lies in the fact that, based on the generalization of world experience in the development of promising requirements for the skills and competence of managers and specialists of national hydrometeorological services, it was found that when forming personnel management strategies, on the one hand, requirements are put forward for a wide range of competencies of personnel who own both traditional technologies and innovative technologies for processing and interpreting information based on IT. On the other hand, hydrometeorological services impose increased demands on themselves, which is manifested in strategic aspirations to form a holistic motivation system for their employees, covering all levels of human needs, according to substantive theories of motivation – from economic competitiveness to opportunities for training, development and self-expression.Conclusion. The practical significance of the obtained results lies in the possibility of using world experience in developing promising requirements for the skills and competence of managers and specialists of national meteorological services when forming strategies for personnel management of hydrometeorological structures of the Roshydromet system.

Lessons learnt from a real-time attribution and contextualisation trial in a National Meteorological and Hydrological Service

Abstract When a record hot month occurs, timely and credible attribution and contextualisation information can enhance public understanding and future preparedness. This is particularly effective if provided in real time by a National Meteorological and Hydrological Service (NMHS). Many NMHSs are working to integrate research-based attribution methods into their operational services. In this study, researchers and climate service staff collaborated to assess the feasibility of delivering such information swiftly and aligned with standard NMHS data and procedures. The record warm July (winter) temperatures of Tasmania, Australia in 2023 were chosen to illustrate the trial. Rapid results were available three days after the event. Approximately half of the unusual warmth was attributed to climate change, with the likelihood of breaking the previous record at least 17 times higher in the current climate compared to a stationary pre-industrial climate (14% vs. 0.4%). The warming trend became evident in the 1980s, and by 2060, average July temperatures in Tasmania match the record temperature of July 2023 under a high emissions scenario. However, average July minimum temperatures were not well modelled, necessitating the addition of a higher-resolution forecast-based attribution method. In subsequent analysis, almost all the forecast temperature anomaly, and reduced storm activity, was attributable to climate change. Statistical analysis revealed that a weak El Niño partly offset the unusual warmth. To expedite these additional approaches, information drawn from real-time forecasts could be used. Lessons learnt from this trial include technical improvements to align better with NMHS protocols including using consistent datasets and baselines, and refining and automating the method suite. Logistical and communication enhancements included training staff to run the suite, improving communication materials, and developing delivery channels. These learnings provide key considerations for NMHSs as they move towards providing timely and credible climate attribution and contextualisation information as part of their operational services.

ALICENET – an Italian network of automated lidar ceilometers for four-dimensional aerosol monitoring: infrastructure, data processing, and applications

Abstract. ​​​​​​​Vertically resolved information on aerosol particles represents a key aspect in many atmospheric studies, including aerosol–climate interactions and aerosol impacts on air quality and human health. This information is primarily derived by lidar active remote sensing, in particular with extensive networks currently in operation worldwide. In Italy, the Institute of Atmospheric Sciences and Climate (ISAC) of the National Research Council (CNR) established the ALICENET network of automated lidar ceilometers (ALCs) in 2015. Since then, ALICENET has grown as a cooperative effort of Italian institutions dealing with atmospheric science and monitoring, and it currently includes instruments run by regional environmental protection agencies, universities, research centres, and private companies. In the current configuration, the network makes use of both single-channel ALCs and dual-channel, polarisation-sensitive-system ALCs (referred to as PLCs). The systems operate in very different environments (urban, coastal, mountainous, and volcanic areas) from northern to southern Italy, thus allowing the continuous monitoring of the aerosol vertical distribution across the country. ALICENET also contributes to the EUMETNET programme E-PROFILE, filling an Italian observational gap compared to other EU member states, which generally run extended ALC networks through national meteorological services. In this work, we present the ALICENET infrastructure and the specifically developed data processing centralised at CNR-ISAC, converting raw instrumental data into quantitative, quality-controlled information on aerosol properties ranging from attenuated backscatter to aerosol mass and vertical stratifications. This setup allows us to get insights into the 4D aerosol field over Italy with applications from near-real-time monitoring to long-term analyses, examples of which are reported in this work. Specific comparisons of the ALICENET products to independent measurements obtained with different techniques, such as particulate matter (PM) concentrations from in situ samplers and aerosol optical depth (AOD) from sun photometers, are also included here, revealing the good performances of the ALICENET algorithms. Overall, ALICENET represents a valuable resource to extend the current aerosol observational capabilities in Italy and in the Mediterranean area, and it contributes to bridging the gap between atmospheric science and its application to specific sectors, among which are air quality, solar energy, and aviation safety.

Dengue Dynamics: Modelling Spread and Environmental Interactions

The objective of this study is to analyze the behavior of dengue fever in the city of La Plata during one year, considering temperature as an environmental factor, its influence on the mosquito population and the transmission of the DENV virus (which causes dengue fever, also known as dengue fever). To become aware of the magnitude of the problem in the future, and using the temperature estimated by the global warming trend, we sought to project an increase in average annual temperatures for the coming years, and thus estimate the impact on the spread of dengue fever. The Netlogo simulation tool was used to model the behavior of a mosquito population and the spread of the dengue virus through contact with the human population. Using official data from the National Meteorological Service, a scenario of spread was simulated for the period November 2022-November 2023, and the increase in temperature due to climate change was projected to simulate how it affects the spread of the virus and the mosquito population, maintaining the same trend for 2024, 2025 and 2030. It was concluded that climate change may generate an expansion in both the size of mosquito populations and their annual activity, leading to the appearance of dengue outbreaks outside the identified warmer seasons.

Economic Assessment of Meteorological Information Services for Capture Fisheries in Taiwan

This study evaluates the economic benefits of meteorological information services (MISs) provided by the Central Weather Administration (CWA) in Taiwan, specifically for Taiwan’s capture fishery industry. Using the contingent valuation method (CVM) and conducting in-person interviews, we collected questionnaires from capture fishery practitioners nationwide to gather their subjective evaluations of the meteorological information services provided by the CWA. Based on these evaluations, we further investigated the respondents’ willingness to pay (WTP) for the CWA’s meteorological information services. An empirical analysis of the bid function was conducted to identify the key factors influencing the respondents’ bidding behavior. The empirical findings indicate that the primary factors affecting bids include subjective perceptions (such as forecast accuracy ratings and the ratings of the impacts on fishing production), working location, fishing vessel tonnage, and fishing methods. The median WTP for Taiwanese capture fishery practitioners is Taiwan dollar (TWD) 2111.12 per person per year. Based on the number of capture fishery practitioners in Taiwan in 2019, the total annual economic value of applying MIS in coastal and offshore fisheries is estimated to be between TWD 376 million and TWD 496 million per year.

Exploring the role of national weather services in climate change knowledge and communication: an international survey

This study investigates the roles, perspectives, and communication strategies of National Meteorological Services (NMS) concerning climate monitoring, climate change projections, and the attribution of meteorological phenomena to climate variations. Using a survey distributed to 131 of the 193 National Meteorological Services affiliated with the World Meteorological Organization, we explore how these entities contribute to and communicate about the science of climate change. The survey targeted their involvement in observing and recording climate data, making climate projections, attributing specific weather events to climate change, and their methods of communicating these issues to the public and government officials. Responses were received from 44 countries, reflecting diverse levels of economic development and capabilities in handling, and disseminating complex climate information. The results show a strong engagement in traditional meteorological tasks, with a varied approach to the scientific study of climate change effects and public communication strategies. This study highlights the critical role of NMS in climate change research and underscores the challenges they face in effectively communicating complex climate information, which is crucial for public understanding and policymaking.

The Impact of Meteorological Factors on Stroke Incidence in the Transdanubian Region of Hungary

Cerebrovascular diseases are the leading cause of death and disability. The epidemiological background and predisposing factors have been the basis of many studies. We aimed to assess the effect of seasonal variability and meteorological factors on stroke incidence in Hungary. National and county-level secondary data were assessed for 2018–2019. We identified stroke with ICD codes I60, I61, I62 (hemorrhagic), I63, I65, and I66 (ischemic). The data were obtained from the University of Pécs Clinical Centre (number of patients per day according to sex and disease subtype, n = 1765). Daily average and maximum wind speed [m/s], precipitation [mm], temperature [°C], and frontal effect [warm/cold/mixed/no effect]) were provided by the Hungarian Meteorological Service. We found that 89.92% of the patients were hospitalized for ischemic and 10.08% for hemorrhagic stroke. We observed a significantly higher number of cases in the other months compared to winter (spring: +35.9%; p = 0.007, summer: +59.0%; p = 0.016, autumn: +36.5%; p = 0.01). In autumn, an increase in temperature increased the incidence of stroke (r = 0.210; p = 0.004). Temperature change affected ischemic stroke incidence (r = 0.112; p = 0.003). In contrast, the number of hemorrhagic stroke cases showed a mild but significant negative association with daily temperature change (r = −0.073; p = 0.049). Overall, a 1 °C temperature change compared to the previous day increased the daily number of admissions by 2.9% (p = 0.017). Air pressure change also affected hemorrhagic stroke incidence (r = 0.083; p = 0.025). Changes in temperature and frontal effects can increase the incidence of stroke. Modern forecasting technology can help the healthcare system prepare for possible increased workloads during critical periods.

The hectometric modelling challenge: Gaps in the current state of the art and ways forward towards the implementation of 100‐m scale weather and climate models

AbstractFor a number of years research has been carried out in several centres which has demonstrated the potential benefits of 100‐m scale models for a range of meteorological phenomena. More recently, some meteorological services have started to consider seriously the operational implementation of practical hectometric models. Many, but by no means all, of the applications are likely to relate to urban areas, where the enhanced resolution has obvious benefits. This article is concerned with the issues that need to be addressed to bridge the gap between research at 100‐m scales and practical models. We highlight a number of key issues that need to be addressed, with suggestions of important avenues for future development. An overarching issue is the high computational cost of these models. Although some ideas to reduce this are presented, it will always be a serious constraint. This means that the benefits of these models over lower resolution ones, or other techniques for generating high‐resolution forecasts, will need to be clearly understood, as will the trade‐offs with resolution. We discuss issues with model dynamical cores and physics–dynamics coupling. There are a number of challenges around model parameterisations, where some of the traditional problems (e.g., convection) become easier but a number of new challenges (e.g., around surface parameterisations) appear. Observational data at these scales present a challenge and novel types of observations will need to be considered. Data assimilation will be needed for short‐range forecasts, but there is currently little knowledge of this, although some of the likely issues are clear. An ensemble approach will be essential in many cases (e.g., convection), but research is needed into ensembles at these scales and significant work on post‐processing systems is required to make the best use of models at these grid lengths.

Decadal prediction centers prepare for a major volcanic eruption

Abstract The World Meteorological Organization’s Lead Centre for Annual-to-Decadal Climate prediction issues operational forecasts annually as guidance for regional climate centers, climate outlook forums and national meteorological and hydrological services. The occurrence of a large volcanic eruption such as that of Mount Pinatubo in 1991, however, would invalidate these forecasts and prompt producers to modify their predictions. To assist and prepare decadal prediction centers for this eventuality, the Volcanic Response activities under the World Climate Research Programme’s Stratosphere-troposphere Processes And their Role in Climate (SPARC) and the Decadal Climate Prediction Project (DCPP) organized a community exercise to respond to a hypothetical large eruption occurring in April 2022. As part of this exercise, the Easy Volcanic Aerosol forcing generator was used to provide stratospheric sulfate aerosol optical properties customized to the configurations of individual decadal prediction models. Participating centers then reran forecasts for 2022-2026 from their original initialization dates, and in most cases also from just before the eruption at the beginning of April 2022, according to two candidate response protocols. This article describes various aspects of this SPARC/DCPP Volcanic Response Readiness Exercise (VolRes-RE), including the hypothesized volcanic event, the modified forecasts under the two protocols from the eight contributing centers, the lessons learned during the coordination and execution of this exercise, and the recommendations to the decadal prediction community for the response to an actual eruption.

Research on dangerous flight weather prediction based on machine learning

Abstract With the continuous expansion of the scale of air transport, the demand for aviation meteorological support also continues to grow. The impact of hazardous weather on flight safety is critical. How to effectively use meteorological data to improve the early warning capability of flight dangerous weather and ensure the safe flight of aircraft is the primary task of aviation meteorological services. In this work, support vector machine (SVM) models are used to predict hazardous flight weather, especially for meteorological conditions with high uncertainty such as storms and turbulence. SVM is a supervised learning method that distinguishes between different classes of data by finding optimal decision boundaries in a high-dimensional space. To meet the needs of this study, we chose the radial basis function (RBF) as the kernel function, which helps to deal with nonlinear problems and enables the model to better capture complex meteorological data structures. During the model training phase, we used historical meteorological observations from multiple weather stations, including temperature, humidity, wind speed, wind direction, and other meteorological indicators closely related to flight safety. From this data, the SVM model learns how to distinguish between normal and dangerous flight weather conditions.

Study on Downscaling Correction of Near-Surface Wind Speed Grid Forecasts in Complex Terrain

Accurate forecasting of wind speeds is a crucial aspect of providing fine-scale professional meteorological services (such as wind energy generation and transportation operations etc.). This article utilizes CMA-MESO model forecast data and CARAS-SUR_1 km ground truth grid data from January, April, July, and October 2022, employing the random forest algorithm to establish and evaluate a downscaling correction model for near-surface 1 km resolution wind-speed grid forecast in the complex terrain area of northwestern Hebei Province. The results indicate that after downscaling correction, the spatial distribution of grid forecast wind speeds in the entire complex terrain study area becomes more refined, with spatial resolution improving from 3 km to 1 km, reflecting fine-scale terrain effects. The accuracy of the corrected wind speed forecast significantly improves compared to the original model, with forecast errors showing stability in both time and space. The mean bias decreases from 2.25 m/s to 0.02 m/s, and the root mean square error (RMSE) decreases from 3.26 m/s to 0.52 m/s. Forecast errors caused by complex terrain, forecast lead time, and seasonal factors are significantly reduced. In terms of wind speed categories, the correction significantly improves forecasts for wind speeds below 8 m/s, with RMSE decreasing from 2.02 m/s to 0.59 m/s. For wind speeds above 8 m/s, there is also a good correction effect, with RMSE decreasing from 2.20 m/s to 1.65 m/s. Selecting the analysis of the Zhangjiakou strong wind process on 26 April 2022, it was found that the downscaled corrected forecast wind speed is very close to the observed wind speed at the station and the ground truth grid points. The correction effect is particularly significant in areas affected by strong winds, such as the Bashang Plateau and valleys, which has significant reference value.

Quantification of Airborne Particulate Matter and Trace Element Deposition on Hedera helix and Senecio cineraria Leaves.

In both developed and developing countries, atmospheric pollution with particulate matter (PM) remains an important issue. Despite the health effects of poor air quality, studies on air pollution are often limited by the high costs of continuous monitoring and the need for extensive sampling. Furthermore, these particles are often enriched with potentially toxic trace elements and organic pollutants. This study evaluates both the composition of atmospheric dust accumulated during a certain timespan on Hedera helix and Senecio cineraria leaves and the potential for their use as bio-monitors. The test plants were positioned near automatic air quality monitoring stations at four different sites with respectively high, moderate and low traffic intensity. The gravimetric deposition of PM10 and PM2.5 on leaves was compared with data recorded by the monitoring stations and related to the weather conditions reported by Argentina's National Meteorological Service. To determine the presence of trace elements enriching the PM deposited on leaves, two analytical techniques were applied: XRF (not destructive) and ICP (destructive). The results indicated that only in the unpaved street location (site 2) did PM10 and PM2.5 concentrations (90 µg m-3 and 9 µg m-3) in the air exceed more than five times WHO guidelines (15 µg m-3 and 5 µg m-3). However, several trace elements were found to be enriching PM deposited on leaves from all sites. Predominantly, increased concentrations of Cd, Cu, Ti, Mn, Zn and Fe were found, which were associated with construction, traffic and unpaved street sources. Furthermore, based on its capability to sequester above 2800 µg cm-2 of PM10, 2450 µg cm-2 of PM2.5 and trace elements, Senecio cineraria can be taken into consideration for adoption as a bio-monitor or even for PM mitigation.

INVESTIGATION OF THE EFFECT OF ANNUAL AVERAGE TEMPERATURE AND PRECIPITATION CHANGES ON CORS-TR STATIONS: THE CASE OF KSTM STATION

In this study, the effects of meteorological changes on the point positioning of CORS-TR stations were investigated. For this purpose, KURU, SINP, BOYT, CORU, CANK, CMLD, KRBK, KSTM stations were selected. The KSTM station was taken as unknown and adjusted based on other stations. Seasonal normal values of KSTM station in Kastamonu province covering the years 2016-2020 were examined in terms of temperature and precipitation amount. These values were determined according to the minimum, maximum and average value criteria by using Türkiye State Meteorological Service data. For the calculations, IGS-standardized RINEX data of the stations for 5 years and 12 months between 2016 and 2020 and for 10 days on the 11th and 20th days of each month were used. All calculations were processed with Leica Geo Office v8.x. The calculated coordinates were compared with the current coordinates of CORS-TR at the same epoch and examined according to annual temperature and precipitation. In the analyzes, it was tested by statistical method whether all measurements were compatible. When it was examined whether the temperature changes were statistically significant, it was observed that the test values were calculated according to the temperature changes were below the test distribution limit at 95% confidence interval. When it was examined whether the precipitation changes were statistically significant, it was observed that the test values were calculated according to the precipitation changes were below the test distribution limit at 95% confidence interval.

Case study of high waves in the South Pacific generated by Tropical Cyclone Harold in 2020

This study highlighted a high wave case by severe tropical cyclone Harold and conducted a simulation with a newly developed wave forecasting system for the South Pacific based on the Japan Meteorological Agency third generation wave model (JMA MRI-III) using the National Center for Environment Prediction Global Forecast System (GFS) winds. Harold was a very intense tropical cyclone (TC) and very high waves up to 10 m affected parts of Vanuatu and Fiji. The model results were reasonable and verified against observations of orbital satellites and a wave buoy at Komave in Fiji. The statistical verifications were carefully analysed. The Root Mean Squared Error (RSME), Scatter Index (SI), Bias and R2 are all showing very impressive results. The new wave forecasting system is the first high resolution operational model at Fiji Meteorological Service (FMS), which covers the whole Fiji area. The system will provide guidance to FMS in preparing marine alerts and warning better and more confidence in providing the marine forecast accurately.

Assesing wave power potential in Nkhatabay, Malawi

Background: This thesis is an investigation of the wave power resource on Lake Malawi, precisely Nkhatabay, focusing on the spatial distribution of wave power of the said places exposed to the highest wave power. Findings: The main objective of this study was to determine the wave potential of Lake Malawi in Malawi in order to allow design and usage of wave energy in Malawi. Methods: The study methodology employed to achieve this main objective entails an analysis of measured wave data recorded at wave recording station along Lake Malawi. Conclusion: The analysis provided a general description of wave power at the location for which wave data exist. From this analysis,it was found that Nkhatabay is exposed to average wave power of approximately 202 KW/m annually. Data was collected from the Department of Climate Change and Meteorological Services (DCCMS), who have implanted a Lake buoy at Usisya in Nkhatabay. The data gathered was analyzed using Microsoft excel and R studio to come up with graphs for proper description of results. Power calculations were conducted as well to determine the output of turbines used with respect to the wave data observed.

Towards Optimising the Derivation of Phenological Phases of Different Crop Types over Germany Using Satellite Image Time Series

Operational crop monitoring applications, including crop type mapping, condition monitoring, and yield estimation, would benefit from the ability to robustly detect and map crop phenology measures related to the crop calendar and management activities like emergence, stem elongation, and harvest timing. However, this has proven to be challenging due to two main issues: first, the lack of optimised approaches for accurate crop phenology retrievals, and second, the cloud cover during the crop growth period, which hampers the use of optical data. Hence, in the current study, we outline a novel calibration procedure that optimises the settings to produce high-quality NDVI time series as well as the thresholds for retrieving the start of the season (SOS) and end of the season (EOS) of different crops, making them more comparable and related to ground crop phenological measures. As a first step, we introduce a new method, termed UE-WS, to reconstruct high-quality NDVI time series data by integrating a robust upper envelope detection technique with the Whittaker smoothing filter. The experimental results demonstrate that the new method can achieve satisfactory performance in reducing noise in the original NDVI time series and producing high-quality NDVI profiles. As a second step, a threshold optimisation approach was carried out for each phenophase of three crops (winter wheat, corn, and sugarbeet) using an optimisation framework, primarily leveraging the state-of-the-art hyperparameter optimization method (Optuna) by first narrowing down the search space for the threshold parameter and then applying a grid search to pinpoint the optimal value within this refined range. This process focused on minimising the error between the satellite-derived and observed days of the year (DOY) based on data from the German Meteorological Service (DWD) covering two years (2019–2020) and three federal states in Germany. The results of the calculation of the median of the temporal difference between the DOY observations of DWD phenology held out from a separate year (2021) and those derived from satellite data reveal that it typically ranged within ±10 days for almost all phenological phases. The validation results of the detection of dates of phenological phases against separate field-based phenological observations resulted in an RMSE of less than 10 days and an R-squared value of approximately 0.9 or greater. The findings demonstrate how optimising the thresholds required for deriving crop-specific phenophases using high-quality NDVI time series data could produce timely and spatially explicit phenological information at the field and crop levels.

Creating High-Resolution Precipitation and Extreme Precipitation Indices Datasets by Downscaling and Improving on the ERA5 Reanalysis Data over Greece

The aim of this study was to construct a high-resolution (1 km × 1 km) database of precipitation, number of wet days, and number of times precipitation exceeded 10 mm and 20 mm over Greece on a monthly and on an annual basis. In order to achieve this, the ERA5 reanalysis dataset was downscaled using regression kriging with histogram-based gradient boosting regression trees. The independent variables used are spatial parameters derived from a high-resolution digital elevation model and a selection of ERA5 reanalysis data, while as the dependent variable in the training stages, we used 97 precipitation gauges from the Hellenic National Meteorological Service for the period 1980–2010. These stations were also used for validation purposes using a leave-one-out cross-validation methodology. The results of the study showed that the algorithm is able to achieve better R2 and RMSE over the standalone ERA5 dataset over the Greek region. Additionally, the largest improvements were noticed in the wet days and in the precipitation over 10 and 20 mm, where the ERA5 reanalysis dataset overestimates the number of wet days and underestimates precipitation over 10 and 20 mm, while geographically, the ERA5 dataset performs the worst in the island regions of Greece. This indicates that the ERA5 dataset does not simulate the precipitation intensity accurately over the Greek region, and using our methodology, we were able to increase the accuracy and the resolution. Our approach delivers higher-resolution data, which are able to more accurately depict precipitation in the Greek region and are needed for comprehensive climate change hazard identification and analysis.