bluuebunny/crossref_metadata_2025 huggingface dataset:
Historical evidence of snowfall variability and trends in the Central and Southern Apennine Mountains
Authors: Capozzi Vincenzo , Serrpaica Francesco , Rocco Armando , Annella Clizia , Budillon Giorgio
Published: July 2024

In recent years, a great deal of attention has been devoted to the study of past snowfall variability worldwide, mainly in mountain regions. The snow, in fact, is a pivotal component of the hydrological cycle and has a relevant impact on the energy budget, controlling the land surface albedo. In addition, the snow strongly affects the complex ecosystems of mountain areas, as well as the biogeochemical cycles. Therefore, considering also the recent climate changes, that are posing serious threats on the cryosphere and mountain regions, it is crucial to recover and analyse historical long-term time series of snowfall to assess its variability and tendencies.For several reasons, many mountain areas remain under-researched. In the Mediterranean, an example in this sense is represented by the Apennine region (Italy). A considerable lack, in fact, exists in the knowledge of the past snowfall variability for this area, although it has a good heritage of past in situ observations.This work presents an analysis of historical snow precipitation data collected in the period 1951-2001 in Central and Southern Apennines. To pursue this aim, we used the monthly observations of the snow cover duration, number of days with snow and total height of new snow collected at 129 stations located between 288 and 1750 m a.s.l.. Such data have been manually digitized from the Hydrological Yearbooks of the Italian National Hydrological and Mareographic Service. The available dataset has been primarily analyzed to build a reference climatology (related to 1971-2000 period) for the considered Apennine region. More specifically, using a methodology based on Principal Component Analysis and k-means clustering, we have identified different modes of spatial variability, mainly depending on the elevation, which reflect different climatic zones. Subsequently, focusing on the number of days with snow and snow cover duration on the ground, we have carried out a linear trend analysis. An overall negative tendency has been found for both variables. For clusters including only stations above 1000 m a.s.l., a significant (at 95% confidence level) decreasing trend has been found in the winter season (i.e. from December to February): −3.2 [─6.0 to 0.0] days/10 years for snow cover duration and −1.6 [─2.5 to ─0.6] days/10 years for number of days with snow. Moreover, in all considered seasons, a clear direct relationship between trend magnitude and elevation has emerged. In addition, using a cross wavelet analysis, we found a close in-phase linkage on decadal time scale between the investigated snow indicators and the Eastern Mediterranean Pattern. For both snow cover duration and number of days with snow, such connection appears to be more relevant in full (i.e. from November to April) and in late (i.e. from February to April) seasons.
Historical Droughts in Catalonia. New database for events from 1700 to 195
Authors: Barriendos Mariano , Barriendos Josep , Cunillera Jordi , Prohom Marc
Published: July 2024

Drought is a prevalent climatic risk in the Mediterranean region. The Mediterranean climate’s specific conditions define a pluviometric pattern characterized by pluviometric deficits in certain seasons of the year. Interannual variability also highlights drought as a frequent phenomenon within the Mediterranean climate.The current context in NE Spain presents a worrisome outlook, with a very severe and persistent drought episode for the period 2021-2024, leading Catalonia into an emergency situation. In order to improve the management and planning of these episodes in the immediate future, it is necessary to increase  knowledge about severe drought episodes occurred in the past, focusing efforts on low-frequency and high-severity events.This study proposes a methodological approach to identify and catalog drought episodes from historical data. It represents an initial effort to compile sufficient information to propose classification criteria and to carry out a first diagnosis of the behaviour of drought at large timescales.The initiative has been undertaken by the Meteorological Service of Catalonia and a team of specialists who have worked with information obtained from documentary and bibliographic sources, spanning the period from 1700 to 1950. Qualitative information has been compiled from municipal and ecclesiastical administrative sources, with a daily resolution: pro pluvia rogations of different levels of severity (from 1 to 5) and direct descriptions of drought impacts. A total of 475 episodes of rainfall deficit have been identified, which can be classified into four different categories according to their duration:- Punctual Droughts (lasting several days).- Meteorological Droughts (lasting several months).- Climatic Droughts (lasting several years).- Megadroughts (a sequence of climatic droughts). Hydrological drought situations, characterized by decrease water levels in rivers and canals, as well as groundwater levels in wells and mines, have been also been identified in response to atmospheric drought. Issues related of agricultural activity, energy production and water supply to the population have been also detected.The collected information allows for a quantitative assessment of each drought episode in some basic aspects such as the duration, affected area and severity.            As for basic results, it can be stated that the current severe drought shares similarities with historical references, such as the episodes observed in  the last decades of the 19th century, the megadrought spanning from 1812 to 1825, or the intense climatic drought experienced from 1750 to 1755.
A study of a damaging hailstorm episode in the Basque Country: The Vitoria-Gasteiz case of July 6, 2023
Authors: Egaña Joseba , Arrillaga Jon Ander , Gaztelumendi Santiago
Published: July 2024

In this work, we present a study of a severe weather event that occurred on July 6, 2023, in the Basque Country, particularly affecting its capital (Vitoria-Gasteiz), where it caused numerous incidents due to hail, including material damage and even injuries from hailstones impacts. A synoptic, mesoscale, and local analysis of this hailstorm event is conducted, utilizing numerical models at different scales and various observations sources, including data from Automatic Weather Stations, disdrometers, and notably radar data available in the area. The ultimate goal of this study is to contribute to a deeper understanding of deep convection and associated hazards in our region.During this episode, the overall situation is characterized by relatively low thermal pressures over the Iberian Peninsula, with southwest winds aloft and a slight trough located to the west. Instability indices indicate a significant level of instability, with lifted index (LI) values below -3 ºC, a CAPE exceeding 600 J/kg, and a TTI around 50 ºC. Within this unstable synoptic environment, highly active convective cores or cells develop during the afternoon, moving from southwest to northeast. However, at certain moments and with certain cells, there is a pronounced west-east displacement, as seen in the particular case of the storm affecting Vitoria-Gasteiz. This storm enters Araba territory from the west and moves from west to east until reaching Vitoria-Gasteiz, maintaining its activity with radar reflectivities of 55-60 dBz, occasionally reaching 60-65 dBz. Subsequently, after passing Vitoria-Gasteiz, the convective system loses activity and shifts northeastward.During the event intense showers accompanied by large hail, up to 4-5 cm in diameter are produced. Precipitation intensities recorded at various network stations range from 20 mm/h to 30 mm/h. This high impact episode causes numerous incidents and extensive damage in Vitoria-Gasteiz, including 11 injuries with head lacerations, damage to thousands of cars, street and road collapses, sewer and factory damage, flooding in commercial premises and garages, etc. Crop damage are also reported in other areas of Araba.
Regional frequency analysis for maximum 5-day precipitation totals using L-moments approach in Slovakia.
Authors: Markovič Ladislav , Faško Pavel , Bochníček Oliver
Published: July 2024

Understanding the patterns of extreme precipitation is crucial for effective water resource management, infrastructure design, and flood risk assessment. This study offers a comprehensive analysis of the maximum annual 5-day precipitation totals (Rx5d) in Slovakia using regional frequency analysis (RFA) to elucidate the probabilistic behavior of these events, essential for informed decision-making amid changing climate patterns. The main objectives were to identify homogeneous regions based on Rx5D, estimate regional frequency distributions, calculate maximum Rx5D for return periods of 5, 10, 20, 50, 100, and 200 years, and map these estimates for Slovakia. The cluster analysis, employing index-flood procedure and Ward's method, identified 14 reasonably homogeneous clusters. Homogeneity and discordancy tests further refined these clusters. The regional frequency distribution for each Rx5D region was determined using L-moment ratio diagrams,  measure and Anderson-Darling tests, resulting in the selection of Gumbel (GUM), Generalized Pareto (GPA), and Generalized Logistic (GLO) as the best-fit distributions for different regions. Our results indicate that, design Rx5D values expected once every 100 years in lowland regions could occur as frequently as once every 25 years in mountainous areas. The most extreme design Rx5D values exceeding 200 mm were observed in the high-elevation mountainous regions, underscoring the heightened risk of extreme precipitation events in these areas.  The study suggests that cluster analysis coupled with L-moments-based regional frequency analysis can effectively derive design rainfall estimates for Slovakia. The developed regional frequency curves are invaluable for estimating return periods of extreme 5-day precipitation events at any location within the study area, proving indispensable for effective flood risk management, infrastructure design, and climate adaptation planning.
Quantifying the Impact of Extreme Storm Events on Aviation Operations over Europe and the USA
Authors: Rapella Lia , Alberti Tommaso , Faranda Davide
Published: July 2024

Extreme storm events have a profound impact on aviation operations, often leading to significant damages and hazardous flying conditions. This study focuses on four prominent storms that affected both Europe and the USA between February 2022 and June 2023, resulting in widespread disruptions to aviation services.By leveraging historical and contemporary data on sea level pressure (SLP), this research applies the attribution methodology outlined by Yiou \cite{Yiou} to compute weather analogues. By means of 6-hourly ERA5 data-sets spanning from 1950 to 2023, split into two distinct 35-year periods—[1950–1984], denoting a factual period,  and [1988–2023], representing a counterfactual period — we identify the cyclone time for each event, designated as the time when cyclones associated with the storms reach their lowest SLP. Thirty best analogue cyclones are selected within defined spatial domains for each period, encompassing all months of the year in one case and only the months of the involved season in the other.Composite maps of SLP and geopotential height anomalies are computed for the time-steps corresponding to the found analogues, assessing differences between the two periods. The analysis extends to precipitation and eddy dissipation rate (EDR), a crucial indicator to assess the level of air turbulence during flights.These comprehensive analyses aim to provide a comprehensive assessment of atmospheric conditions during the studied extreme events, to enhance understanding of the atmospheric dynamics of extreme storm events and their implications for aviation safety, flight operations, and air traffic management. The findings are crucial for developing strategies to mitigate risks and improve adaptation for future storm events in the aviation industry.
Introduction of operational convection permitting ensemble prediction system of North China
Authors: Zhang Hanbin , Xia Yu
Published: July 2024

Abstract: With the development of technology, major operational centers and scientific research institutions have carried out research and development of convection permitting ensemble prediction technology and systems. In order to meet the needs of accurate forecasting in the capital, the Convection permitting ensemble prediction system of North China CMA-BJ-EN v1.0 developed by the Institute of Urban Meteorology was officially put into operation in January 2023. This paper introduces the research and development background and key technologies of the system in detail. The results show that the CMA-BJ-EN v1.0 system has a resolution of 3km convective resolution scale, covering 21 members, and can provide hourly probability prediction results of 48h forecast effectiveness in North China; The system is coupled with advanced technologies such as initial conditon perturbation of ensemble data assimilation, stochastic physical process tendency(SPPT) model perturbation, and has complete pre-processing, initial condition perturbation, model perturbation, and post-processing configuration, which can provide a series of probability prediction products; The qualitative and quantitative evaluation of CMA-BJ-EN v1.0 system and its comparison with NCEP global ensemble forecast system show that the system can effectively grasp several major weather processes in North China, and can obtain local refined probability forecast results relative to global ensemble; The statistical results also show that compared with the NCEP GEFS global ensemble forecast, the CMA-BJ-EN v1.0 forecast system can effectively reduce the RMSE of 2m temperature and 10m wind speed, and can effectively improve the probability forecast score of precipitation forecast. The operation of the system can provide users with effective refined probability forecast reference.Keywords: convection permitting, ensemble prediction system, initial condition perturbation, model perturbation
Bioclimatic conditions in the north-eastern part of the Labrador Peninsula in the second half of the 18th century 
Authors: Chmist Konrad , Araźny Andrzej , Przybylak Rajmund , Wyszyński Przemysław , Singh Garima
Published: July 2024

The bioclimate of the north-eastern part of the Labrador Peninsula was estimated based on meteorological observations from three measuring stations: Nain, Okak and Hopedale. The meteorological measurements were taken by Moravian Missionaries who conducted religious missions in this area.The collected data concerned air temperature, atmospheric pressure and wind speed and direction. Observations were carried out four times a day, at 8 a.m., 12 p.m., 4 p.m. and 8 p.m. The materials used in this work came from three European archives: the Moravian Archives in Herrnhut (Germany), the Moravian Archives at Muswell Hill in London, and the Archives of the Royal Society in London (Great Britain).In order to compare bioclimatic analyses between stations, it was decided to standardise measurement periods. Two time slots were designated:from September 1776 to August 1786 (~98% data coverage): the analysis included data from the Nain and Okak stations at 12:00 local time, due to the bioclimatic conditions being most favourable for humans at that time,
from September 1782 to August 1786, with an annual gap from September 1784 to August 1785 (~93% data coverage): three measuring stations were analysed: Nain, Okak and Hopedale. The time interval was determined based on the shortest data series (i.e., that from the Hopedale station).
In order to present the bioclimatic conditions in the north-eastern part of the Labrador Peninsula, the following indicators were used: Wind Chill Temperature (WCT), Wind Chill Index (WCI) and Insulation Predicted (Iclp). The results were compared with the reference period (1991–2020) from the Nain station.It was found that, in the first of the specified time intervals (lasting 10 years), the bioclimatic conditions were more favourable than in the comparative period. However, it is worth noting that the conditions in Nain were less bearable for humans than in Okak. In the second time period (only 3 years), the most difficult conditions prevailed at the Okak station. Milder conditions were recorded at Nain station, while the most favourable were recorded at Hopedale. This was primarily due to the analysed time period. During the measurements, relatively mild winters were recorded at the Hopedale station, which influenced the final result.At each station during the analysis period, conditions for humans were less favourable than in the reference period. The main role in this matter was played by wind speed, which was much higher in 1991–2020 than in the second half of the 18th century. The work was supported by the National Science Centre, Poland project No. 2020/39/B/ST10/00653.
Multiple linear regression models to predict seasonal occurrences of two important grapevine pest Lepidoptera in Austria
Authors: Kolkmann Kerstin , Blümel Sylvia , Thaler Sabina , Eitzinger Josef
Published: July 2024

Sustainable crop production will in future face further challenges from insect pests due to climate change. Rising temperatures will enable higher overwintering rates and accelerate the development of thermophilic insects, leading to increased damage risks for regional crop production systems. These changes pose problems for optimum timing of monitoring and control measures, which could be countered with improved and new prediction models.Within the ACRP-Project RIMPEST[1] new prediction models were developed for the European grapevine moth, Lobesia botrana (Denis & Schiffermüller) and the European grape berry moth, Eupoecilia ambiguella (Hübner) (Lepidoptera: Tortricidae) in Austria including monitoring data from 60 selected monitoring sites and measured weather data from adjacent reference weather stations in the period 1980 to 2023. Stepwise multiple linear regression (MLR) analysis was applied to generate prediction models for the first seasonal occurrence of the different developmental stages (egg, larvae, adult) of the first and second generation of L. botrana and E. ambiguella. As input data for the MLR analysis nine processed weather parameters, different calculation periods and the DOYs (day of year on which the first seasonal occurrence was observed) were used.The performance evaluation of the six generated MLR models for predicting the different generations and developmental stages of L. botrana resulted in an R2 of 0.51 to 0.92, a RMSE of 2.18 to 3.97 and an average prediction range of 1.90 days too early to 1.40 days too late. For E. ambiguella the validation resulted in an R2 of 0.33 to 0.69, a RMSE of 3.48 to 4.15 and an average prediction range of 2.85 days too early to 1.00 day too early. The MLR models for E. ambiguella first generation egg and larvae were not sufficiently validated as too few datasets were available.The implementation of the new MLR models for impact assessments under regional climate scenarios can help to determine the potential future risks of L. botrana and E. ambiguella occurrence in Austrian wine-growing regions. The inclusion of future observation data into the analysis, especially from years with extreme weather events, can further improve the prediction accuracy of the MLR models. 
[1] ACRP-13th Call Project RIMPEST (KR20AC0K17957) ("The effect of changing climate on potential risks from important insect pests on plant production in Austria and related adaptation options").
https://www.klimafonds.gv.at/report/acrp-13th-call-2020/
https://rimpest.boku.ac.at/
https://www.ages.at/en/research/project-highlights/rimpest
ILS+Urban: an offline land-surface process model for global urban climate and building energy simulations 
Authors: Takane Yuya , Nitta Tomoko , Adachi Sachiho A. , Yoshimura Kei , Nakano Masuo , Nakayoshi Makoto , Onomura Shiho , Crawford Ben
Published: July 2024

We have developed ILS+Urban: a coupled model of an offline land-surface model (ILS) and an urban canopy and building energy model (SLUCM+BEM) for global urban climate and energy research. The ILS is an offline land-surface model developed by Nitta et al. (2020) that includes MATSIRO (Takata et al. 2003), a land-surface model for the global climate model MIROC5. The SLUCM+BEM is a new parametrisation for urban climate and building energy simulations developed by the authors (Takane et al. 2024), which can simply simulate anthropogenic heat from buildings (QFB) and electricity consumption (EC) from human activities. The model could reproduce urban air temperature and EC well in the Tokyo Metropolitan Area. We have implemented the SLUCM+BEM in the ILS, allowing us to simulate global urban climate and building energy with relatively low computational resources in offline mode. A test simulation of ILS+Urban shows that QFB and EC tend to be quantitatively high throughout the year in the Middle East. In the near future, we will implement a global urban database (e.g. global LCZ, anthropogenic heat emissions and morphology, air-conditioning adoption rate) and new technology parameterisations (e.g. EV, PV and heat pump water heaters) for global urban climate and building energy projections and countermeasures for urban heat and energy savings & generation. In addition, the ILS+Urban will be coupled with global climate models (e.g. MIROC and NICAM).
References:Nitta et al. (2020) PEPS, 7, 68.Takane et al. (2024) ESS Open Archive (under review), https://doi.org/10.22541/essoar.170960070.07397688/v1Takata et al. (2003) GPC, 38, 209–222.
Structural characteristics of deep convection over the Tibetan Plateau from the measurement of GPM
Authors: Wang Rui
Published: July 2024

Utilizing GPM observations, ERA5 reanalysis data and topographic data, the structural characteristics of non-penetrating and penetrating deep convection during summer over the Tibetan Plateau are studied. It reveals significant spatiotemporal differences between the two types of deep convections. Notably, the non-penetrating deep convection exhibits higher frequencies and near surface rain rates compared to penetrating deep convection. The mean rain top height of non-penetrating and penetrating deep convection ranges between 15-16 km and 18-19 km, respectively. Moreover, the radar reﬂectivity peaks near the surface for both types while the latent heat release predominantly occurs around the middle of troposphere (near 10 km). The vertical distribution of non-penetrating deep convection raindrop particle concentrations (about 30) remain uniform (5-15 km), with larger raindrop diameters near surface (2.0-2.5 mm). In contrast, the penetrating deep convection exhibits particle concentrations below 10 km and near the rain top (19-20 km), with smaller raindrop diameters (1.0-1.5 mm). Additionally, significant differences in the thermal structure of the two types of deep convection are observed. Non-penetrating deep convection displays a more pronounced positive anomaly of temperature before and after occurrence, attributed to latent heat effects, while the cooling thickness near the tropopause for penetrating deep convection is thinner. Slight dehydration near the tropopause is noted after penetrating deep convection. The evident descent of tropopause height and sharp increase in CAPE before convection are conducive to the occurrence of penetrating deep convection. These results enhance our comprehension of the macro-micro structure of deep convection and the interaction between deep convections and corresponding thermal structures over the Tibetan Plateau.
Postprocessing multi-model ensemble temperature forecasts using Distributional Regression Networks
Authors: Casellas Masana Enric , Miró Cubells Josep Ramon , Moré Pratdesaba Jordi
Published: July 2024

Uncertainty in numerical weather prediction (NWP) models can arise from various sources, such as initial conditions or model parameterizations. Ensemble forecasts, typically generated through perturbed initial conditions or diverse model physics, help address and quantify the uncertainty inherent in raw NWP models. However, these forecasts may still contain biases and dispersion errors, traditionally mitigated using non-homogeneous Gaussian regression (Ensemble Model Output Statistics, EMOS) (Gneiting et al., 2005). Nevertheless, emerging machine learning techniques, like Distributional Regression Networks (DRN) (Rasp and Lerch, 2018), are capable of handling nonlinear relationships between predictors and forecast distributions often yielding similar or superior results. 

At the Meteorological Service of Catalonia (SMC), a Poor Man’s Ensemble (PME) composed by 12 members is constructed using 8 different models: Arome, Arpege, Bolam, ECMWF-HRES, Icon, Moloch, Unified Model, and WRF. These models vary in spatial resolution and are interpolated to a 1 km grid using a lapse-rate correction methodology, accounting for altitude differences between model orography and 1 km digital elevation model (Sheridan et al., 2010). 

The postprocessing of this multi-model ensemble is conducted at point station locations utilizing data from the SMC automatic weather station network as ground truth. A benchmark methodology, EMOS, is applied using an IMPROVER (Roberts et al., 2023) module to calculate a calibration for each station and lead time of the ensemble. The forecast of each model is set as a predictor variable, rather than the commonly used mean and standard deviation of the ensemble. This approach is then compared with a single DRN for each lead time, incorporating all stations via an embedding technique, and using the same predictors. Results indicate a comparable but generally improved performance for DRN compared to EMOS. 

  

References 

Gneiting, T., Raftery, A. E., Westveld, A. H., & Goldman, T. (2005). Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Monthly Weather Review, 133(5), 1098-1118. 

Rasp, S., & Lerch, S. (2018). Neural networks for postprocessing ensemble weather forecasts. Monthly Weather Review, 146(11), 3885-3900. 

Roberts, N., Ayliffe, B., Evans, G., Moseley, S., Rust, F., Sandford, C., ... & Worsfold, M. (2023). IMPROVER: the new probabilistic postprocessing system at the Met Office. Bulletin of the American Meteorological Society, 104(3), E680-E697. 

Sheridan, P., Smith, S., Brown, A., & Vosper, S. (2010). A simple height‐based correction for temperature downscaling in complex terrain. Meteorological Applications, 17(3), 329-339. 
 
The understanding of global warming influences on the temperature behaviour on mountainous areas
Authors: Miró Josep Ramón , Casellas Enric , Peña Juan Carlos , Pepin Nick
Published: July 2024

The temperature behavior in mountainous regions at mid-latitudes is particularly susceptible to the effects of global warming. Within this context, it is crucial to comprehend and quantify the influence of changes in synoptic patterns at higher resolution scales. This study focuses on a Pyrenean valley (north-east of Iberian Peninsula) prone to the formation of cold air pools (CAPs). It is established that CAPs predominantly form under stable conditions. However, deep and enclosed valleys frequently encounter decoupling situations, leading to unexpected variations in climatological temperature profiles. Consequently, understanding how global warming will impact temperatures at various altitudes in the context of Elevation Dependent Warming becomes increasingly challenging.In this study, two temperature transects have been utilized: one covering a closed valley prone to CAP formation and one in an open valley with drainage. Each transect is composed of six temperature and humidity sensors taking data every 30 minutes, covering a range of heights of approximately 1200 m between altitudes of 1000 m and 2200 m. The sensors were set up in 2011 and have been recording data since then, which allows for a first approximation of climate trends in mid-latitude mountain ranges.In contrast with applying purely statistical relationships based on correlations, in this case, a causal approach has been adopted to quantify how the anticipated synoptic variability is transmitted to the differences between the vertical temperature profiles previously defined. After constructing some causal relationships among variables, this has been utilized to infer changes in CAP behavior under different climate change scenarios. This methodology offers a robust framework for assessing the potential impacts of global warming on mountainous regions, providing valuable insights for future climate adaptation and mitigation efforts.
A skilful 24-hour rainfall forecast for Africa based only on tropical wave observations
Authors: Rasheeda Satheesh Athul , Knippertz Peter , Fink Andreas
Published: July 2024

Numerical Weather Prediction (NWP) models generally underperform compared to simpler climatology-based precipitation forecasts in northern Tropical Africa—in some regions, even after statistical postprocessing. Recently developed Artificial Intelligence (AI) weather models show promise in forecasting various meteorological variables, but so far, they largely avoid precipitation forecasts due to its complex nature. However, recent studies have demonstrated the efficacy of a logistic regression model trained on past days' rainfall data to predict daily rainfall occurrences, outperforming NWP ensemble forecasts by leveraging coherent rainfall patterns driven by synoptic-scale forcings like African Easterly Waves (AEWs). AEWs and other tropical waves play a crucial role in modulating synoptic-scale rainfall in tropical Africa, yet their explicit utilization for predicting daily rainfall amounts remains unexplored.The present study addresses this gap by employing two machine-learning (ML) models—gamma regression and convolutional neural network (CNN)—trained solely on tropical wave predictors derived from satellite-based gridded precipitation data from Global Precipitation Measurement Integrated Multi-satellite Retrievals (GPM IMERG) to predict daily rainfall amounts. The predictor variables are computed from the local amplitude and phase information of seven types of tropical waves at the target and neighbouring grid points at 1° spatial resolution. The ML models are combined with the recently introduced Easy Uncertainty Quantification (EasyUQ) method to generate calibrated probabilistic forecasts, which are then compared with three benchmarks: a climatology-based forecast (Extended Probabilistic Climatology- EPC15), the European Centre for Medium-Range Weather Forecasts (ECMWF) operational ensemble forecast (ENS), and a probabilistic forecast derived from the ENS control member using EasyUQ (ENS EasyUQ). Our findings reveal that the ENS forecast exhibits poor skill relative to the EPC15 forecast across most parts of tropical Africa, primarily due to high miscalibration. While the ENS EasyUQ forecast shows considerable improvement over the ENS forecast, only marginal enhancement is achieved compared to the EPC15 forecast over land regions. In contrast, both gamma regression and CNN forecasts significantly outperform the benchmarks in most areas of tropical Africa and fail to achieve statistical significance only in the arid regions in the far North and over the equatorial Atlantic Ocean. Overall, the present study highlights the potential of ML models trained solely on tropical wave predictors to enhance daily precipitation forecasting in tropical Africa, offering valuable insights for improving operational forecasting systems in the region.
Daily and hourly statistical downscaling of CMIP6 climate scenarios for DISTENDER case studies
Authors: Monjo Robert , Royé Dominic , Redolat Darío , Paradinas César , Prado-López Carlos , San José Robert , Pérez-Camanyo Juan Luis , Ahrens Bodo , Ribalaygua Jaime
Published: July 2024

Downscaling approaches are a key to translate global climate change projections to regional and local climate scenarios required in adaptation planning. A cutting-edge statistical method was developed under the framework of the European DISTENDER project to produce climate scenarios (1981-2050) of a large number of variables, including precipitation and temperature, at a daily scale for two extensive regions (Austria and the Spanish-Portuguese EURAF region) and at a hourly scale for three small regions: Guimaraes (Portugal), Metropolitan City of Turin (CMTo, Italy) and The North-east of The Netherlands (HUAS). Our method consisted of three steps: 1) Parametric quantile mapping; 2) hourly transfer function; and 3) Geostatistical downscaling. In the first step, a parametric quantile mapping (Monjo et al. 2014) was used to locally transfer reference probability distribution to the Historical and the four main SSP experiments (SSP1-2.6, SSP2-4.5, SSP3-6.0, SSP5-8.5) of three CMIP6 Earth System Models at a daily scale. To make this, ERA5-Land reanalysis data (0.073°×0.073°) was used as a reference. In the second step, for each modeled (targeted) day, the most analogous past day was selected from the reanalysis by comparing their spatial thermal patterns to each targeted day of every climate projection (from the first step) and then linear transfer functions were applied from the maximum/minimum values of each projected day to the hourly curve of its analogous day, so producing a hourly climate projection of that targeted day at  the reference 0.073°×0.073° grid. The third step is a purely geostatistical technique with multi-linear AIC-based stepwise regression, fitting high-resolution predictors (land-use, geographical and topographical parameters), with a final bilinear interpolation for the residual errors. The generated climate simulations adequately passed the Kolmogorov-Smirnov test for the historical period. Projections showed a reduction of total precipitation amount in summer (CMTo) and autumn (HUAS and Austria) between 10 and 20%, even decreasing more than 40% during the warmest months in Guimaraes and EURAF. Spring will experience an increase of precipitation up to 20% in CMo, while winter will be 10-30% wettest during the winter season in Guimaraes and HUAS. Maximum temperature will increase up to +3.5ºC in Guimaraes and Austria, and up to +5ºC in CMTo, HUAS and Euraf during summer under the SSP5-8.5 scenario. The developed techniques are ready to be transferred to future projects on climate modeling and environmental applications.
From deterministic forecasts to probabilistic impact-based forecasts in meteorology: Theory and practice
Authors: Bukowski Filip
Published: July 2024

Modern weather forecasts and warnings are essential for billions of people around the globe, providing information about daily conditions as well as possible hazards. Despite the significance, they struggle with the challenge of in-complete accuracy in communication due to several factors, such as atmospheric unpredictability, data interpretation, uncertainty of composing forecasts, and forecast interpretation (Gill et al., 2008, p. 6). This study critically examines the prevalent deterministic approach in public meteorological communication, often presenting predictions as definite. The author explores the necessity for a shift towards uncertainty-informed impact-based forecasts, advocated by the World Meteorological Organization (WMO, 2021). Through multidisciplinary inquiry, this research reveals deterministic forecasts' limitations in conveying uncertainty sources, eroding public trust when forecasts do not align with reality, subsequently hindering decision-making (Burgeno and Joslyn, 2023). In contrast, probabilistic forecasts with transparent uncertainty margins might offer a more nuanced depiction of atmospheric variability, fostering public confidence and reducing bias adjustments. A multidisciplinary approach from cognitive studies, meteorology, and geography is employed to understand how communication induces behavioural responses to ambient and severe weather.Embracing uncertainty not only enhances forecast accuracy but also facilitates informed prioritisation of precautionary measures, bolstering societal resilience to weather-related hazards. Moreover, integrating uncertainty communication into impact-based forecasts informs broader activities such as transportation planning and emergency preparedness. This is especially important in the case of unlikely yet high-disruption events, as well as highly likely yet limited-disruption events.The current overview of forecast communication practices from meteorological institutes and agencies across Europe is presented and scrutinised against the existing theoretical basis. Accessible strategies for communication advancement are discussed, and possible future solutions in personalised forecasting are theorised. From a wider perspective, this research aims to guide informed activity in space and in relation to transport choices, ad-hoc plan adjustment, and protective action towards self and personal property.
Towards a Future Forecast Process – How to better utilise human knowledge and intuition in a world of too much information
Authors: Ramsdale Steven
Published: July 2024

The professional journey of meteorologists working in the domain for more than ten years is likely to have included a huge increase in the quality and quantity of available data for decision making. In numerical modelling this has included the move from coarse resolution (>40km) global models to high resolution and even sub km scale convection permitting models as well as the increasing acceptance and use of ensemble systems to aid representation of uncertainty. There is also an increasing demand for forecasts of the impact, not solely occurrence, of weather and so further data such as demography, land use and event timelines must be interrogated to understand the complex combination of factors.Whilst this increase in data and customer expectation has occurred the operational meteorologist forecast process has remained much the same in framework to that of decades ago – a transition through scales from an initial assessment of the broadscale atmospheric state to an appropriate level of reliable detail at the meso or microscale.The result is that operational meteorologists become highly adept at weather regime recognition to understand potential hazards and uncertainties without needing to access the full range of data available. This knowledge is then used to explore what the meteorologist judges to be the most important parts of the forecast in greater detail before making final forecast and warning decisions. This process has served the community well but the increasing pressures on the profession to make ever more efficient and accurate decisions with the increasing data volumes can lead to information overload and the reliance on familiar, but not necessarily the highest value, data sources and parameters.Machine Learning, often colloquially referred to under the umbrella term of Artificial Intelligence (AI), presents an opportunity to update the forecast process utilising the ability to effectively process large multidimensional datasets along with previous human-based decisions, expertise and downstream impacts. The combination of this data and knowledge brings the potential to assess the weather from a hazard specific point of view, allowing recommendation of forecast times, locations or tools for final human decision-making. Research is underway into this application of machine learning, using an archive of issued severe weather warnings and coarse resolution weather data. When applied to the forecast process, along with the caveat that forecast data is not perfect and so human intervention and intuition remains vital to effective decision making, this helps envision a future state where operational meteorologists focus on specific highlighted hazards leading to more efficient utilisation of time and effort. This future state would allow more efficient focus of time where it matters, allowing for more effective decision making and releasing meteorologist time to use their expertise elsewhere other than in product generation. 
Towards automated and global homogenization procedures for early instrumental temperature data.
Authors: Lundstad Elin
Published: July 2024

Homogenization in climate research means the removal of non-climatic changes. This presentation describes the homogenization of the early instrumental dataset (HCLIM; https://doi.pangaea.de/10.1594/PANGAEA.940724) of monthly mean temperature time series. New homogenization algorithm validation methodology is assessed here on early instrumental data, and its use to assess the skill of three different algorithms, when applied to early instrumental data.This study addresses the challenge of homogenizing early instrumental meteorological observations spanning 365 years to accurately reconstruct historical climate change. Manual homogenization methods are time-consuming and prone to errors, necessitating an exploration of automated alternatives. We assess three current automatic homogenization algorithms—CLIMATOL, PHA, and BART—utilizing the global early instrumental dataset HCLIM, covering discontinuous monthly temperature records from 1658 to 2021, with record lengths varying from 15 to 260 years. Our analysis reveals significant differences in break frequency among algorithms, with BART detecting eight times more breaks than CLIMATOL and PHA. The ratio of homogeneous series also varies notably: PHA at 85%, CLIMATOL at 70%, and BART at 31%. Additionally, we evaluate algorithm performance by comparing detected records with collocated reference records from the 20CRv3 reanalysis product. Moreover, we identify certain data points unsuitable for homogenization due to missing neighbouring stations or apparent outliers, while highlighting their potential for identifying extreme climatic events. This research underscores the importance of rigorous assessment and validation of automated homogenization methods in historical meteorological records analysis. Furthermore, we find that both homogenization and the use of 20CRv3 are indispensable for refining results. By comparing the homogenized datasets with a reanalysis dataset, the results reveal that some data points necessitate homogenization, while others do not require this correction based on the reanalysis alone. Furthermore, the study demonstrates that utilizing three homogenization tools in conjunction with the reanalysis dataset yields optimal results for the early instrumental dataset Hclim.
Operational and prototype ensemble clustering post-processing approaches at the Met Office
Authors: Neal Robert , Titley Helen , Sherwin Rebekah , Willington Steve , Moseley Stephen , Roberts Nigel , Boykin Kristine , Methven John , Frame Tom
Published: July 2024

Ensemble clustering is an efficient ensemble post-processing approach that distils an ensemble forecast into its prevalent forecast scenarios by grouping similar ensemble members together - something which is increasingly important in a world where ensemble data volumes are rapidly increasing. The application of a suitable clustering method combined with appropriate forecast visualisation allows a forecaster to effectively focus on the key possible outcomes, simplify the message and characterise and communicate forecast uncertainty more easily. For example, output may be presented as (1) probabilities of each cluster occurring, (2) representative or central members from each cluster showing alternative forecast directions, and (3) probabilities of threshold exceedance under each cluster.Operationally, the Met Office runs a probabilistic weather pattern forecasting tool called Decider (Neal et al., 2024), where ensemble members are clustered according to their allocation to one of 30 predefined weather patterns (circulation types). This allows for changes in the large-scale circulation to be identified at a range of lead times and is useful for many downstream applications where the same weather patterns have been related to specific impacts. To be used alongside this, a prototype feature-based clustering approach is being trialled, which is the focus of this talk. Here, k-medoids clustering is applied to Fractions Skill Score (FSS) distances between members, using identified features in each member. These features may represent areas of hazardous weather or mesoscale or synoptic features, such as areas of heavy rainfall, damaging wind speeds, or weather fronts. The methods being trialled follow those initially developed by Boykin (2022) in PhD work at the University of Reading in collaboration with the Met Office, and include several post-processing steps. These steps are designed to (1) determine spatial distances between objects and cluster on those distances, (2) identify an optimal number of clusters, (3) identify windows of interest where clusters become more distinct, and (4) identify representative members within each cluster, within the time window, to provide plausible forecast scenarios or evolutions with associated probabilities. Multiple configurations of the prototype feature-based clustering approach have been trialled and early results will be discussed.ReferencesBoykin, K.A. (2022) Extracting likely scenarios from Ensemble Forecasts in real time. PhD in Atmosphere, Oceans and Climate, Department of Meteorology, University of Reading, DOI: 10.48683/1926.00111270.Neal, R., Robbins, J., Crocker, R., Cox, D., Fenwick, K., Millard, J., Kelly, J. (2024) A seamless blended multi-model ensemble approach to probabilistic medium-range weather pattern forecasts over the UK. Meteorological Applications, 31(1), e2179.
A case study of rainfall evaporation using a simple drop size distribution column model and Micro Rain Radar observations
Authors: Bech Joan , García-Benadí Albert , Udina Mireia , Polls Francesc , Peinó Eric , Paci Alexandre , Boudevillain Brice
Published: July 2024

Rainfall evaporation beneath cloud base level is a potential factor causing sub estimation of weather radar quantitative precipitation estimates (QPE), particularly at mid and long ranges in arid or semi-arid conditions. This effect is studied using observational data from the “Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment” (LIAISE) field campaign, part of the Global Energy and Water Exchanges (GEWEX) programme, which took place in the Eastern Ebro basin (NE Spain) in 2021. The objective of the study is to assess rainfall evaporation comparing a simple model with field campaign Micro Rain Radar (MRR) observations during LIAISE. The model describes the temporal evolution on a column of a drop size distribution (DSD) considering both sedimentation and evaporation. Ground based automated surface observations collocated with an MRR and a PARSIVEL disdrometer at three sites were used to identify possible events of rainfall evaporation (with precipitation and low relative humidity) and MRR data was processed with the RaProM and RaProM-Pro software (https://doi.org/10.3390/rs12244113, https://doi.org/10.3390/rs13214323) to compute radar reflectivity Z, liquid water content LWC, or precipitation type. Profiles of Z and LWC were examined with Contour Frequency Analysis Diagrams and the simple DSD column model. The case study shows the evolution of observed vs modelled DSDs with and without considering evaporation effects. Results indicated clearly the importance of including the evaporation to describe the evolution of the DSD during the event. This study was supported by Spanish projects WISE-PreP (RTI2018-098693-B-C32), ARTEMIS (PID2021-124253OB-I00) and the Water Research Institute (IdRA) of the University of Barcelona.