Accurate detection of precipitation on a global scale is essential for advancing our understanding of the hydrological cycle and improving climate models. This study evaluates the performance of the Rain Masking Algorithm (RMA), developed for NASA's Micropulse Lidar Network (MPLNET), in detecting rainfall events and distinguishing them from non-rain events over multiple years. The RMA's effectiveness was validated against data from co-located disdrometers at two distinct MPLNET sites: the Goddard Space Flight Center (GSFC) in the United States and Universitat Polit & egrave;cnica de Catalunya (UPC) in Barcelona, Spain. Comparisons were also conducted with precipitation retrievals from the Integrated Multi-Satellite Retrievals for GPM (IMERG) project. Results indicate that the RMA is highly effective at detecting rain events, outperforming IMERG in sensitivity and accuracy at both sites, and demonstrating also unique capability in distinguishing virga, precipitation that evaporated before reaching the ground (not considered in the intercomparison). However, the algorithm shows limitations in identifying low-intensity precipitation and occasionally records false positives due to transient atmospheric artifacts. These results underscore the potential of the RMA in advancing the validation of satellite precipitation data from the ground, which is advantageous for the upcoming ESA-JAXA EarthCARE mission. Although the current analysis does not include EarthCARE data, we present the performance of RMA and a corresponding matchup strategy that are intended to facilitate next validation efforts for EarthCARE's precipitation data. This work also highlights the RMA as a promising tool for refining global precipitation monitoring and advancing meteorological and climate forecasting accuracy.

Accurate classification of precipitation phase (liquid, mixed, or solid) is essential in high mountain environments, where rapid changes in elevation can lead to abrupt phase transitions over short distances, significantly affecting hydro-meteorological, ecological, and socio-economic activities. However, most existing classification schemes have not been evaluated over long periods using real observational data, but mainly through simulations. This study addresses this gap by introducing a new methodology based on X-band polarimetric radar and by validating it against real precipitation events over an extended time period. The machine learning model is trained and tested using a four-year dataset including X-band radar, Micro Rain Radar, disdrometer, and temperature profile data from the Grenoble region (French Alps). To improve the classification accuracy, three temperature profile sources were tested: lapse rates obtained from automatic weather stations, interpolation of the temperature profile from the freezing level detected by the Micro Rain Radar, and temperature profiles from the operational AROME model forecast. Three different phase classification schemes were tested: two existing schemes based on fuzzy-logic, and the new method based on random forest. Results show that the random forest method, trained with radar polarimetric variables, AROME temperature profiles, and target labels derived from Micro Rain Radar observations, achieves the highest accuracy. Despite the overall good classification results, limitations persist in identifying mixed-phase precipitation due to its transitional nature and vertical variability. Feature importance analysis indicates that temperature is the most influential variable in the classification scheme, followed by reflectivity factor measured in the horizontal plane (Ze) and differential reflectivity (Zdr). This methodology demonstrates the potential of combining machine learning techniques with multi-instrument observations to improve hydrometeor classification in complex terrain. The approach offers valuable insights for operational forecasting, water resource management, and climate impact assessments in mountainous regions.

Tornadoes are generally small and short-lived, making direct wind measurements scarce and complicating wind velocity estimation. To overcome this problem, tornado intensities have been rated indirectly using damage scales such as the Fujita (F) scale, the Enhanced Fujita (EF) scale or the International Fujita (IF) scale, recently proposed by the European Severe Storms Laboratory. The IF-scale increases the number of applicable damage indicators outside of the United States compared to the F and EF scales, accounting for building sturdiness, tree characteristics, and frequently affected elements such as vehicles and outdoor furniture. This study applies the new IF-scale to the Catalonia tornado database (2000–2024, including 127 tornadoes) and selected historical events (1890–1999), using field surveys, photographs, press reports, and social media data. Rerating the 2000–2024 database revealed substantial adjustments for weak tornadoes: only 56

The Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) campaign examined the impact of anthropization on the water cycle in terms of land-atmosphere-hydrology interactions (Boone et al. 2025). The objective of this study is to assess the effects of irrigation on the atmosphere and on precipitation in WRF model simulations during the LIAISE Special Observation Period in July 2021 (LIAISE-2021 SOP). Comparisons between simulations and observations show better verification scores for air temperature, humidity and wind speed and direction when the model included the irrigation parameterization, improving the model warm and dry bias at 2 m over irrigated areas. Other changes found are the weakening of the sea breeze circulation and a more realistic surface energy partitioning representation. The boundary layer height is lowered in the vicinity of irrigated areas, causing a decrease in the lifting condensation level and the level of free convection, which induce increases in CAPE and CIN. Precipitation differences between simulations become relevant for smaller areas, close to the irrigated land. When convection is parameterized, simulations including irrigation tend to produce a decrease in rainfall (negative feedback) while convection-permitting simulations produce an increase (positive feedback), although the latter underestimates substantially the observed precipitation field. In addition, irrigation activation decreases the areas exceeding moderate hourly precipitation intensities in all simulations. There is a local impact of irrigated land on model-resolved precipitation accumulations and intensities, although including the irrigation parameterization did not improve the representation of the observed precipitation field, as probably the precipitation systems during LIAISE-2021 SOP were mostly driven by larger scale perturbations or mesoscale systems, more than by local processes (Udina et al. 2024). Results reported here not only contribute to enhance our understanding of irrigation effects upon precipitation but also demonstrate the need to include irrigation parameterizations in numerical forecasts to overcome the biases found. This research has been funded by projects WISE-PreP (RTI2018-098693-B-C32), ARTEMIS (PID2021-124253OB-I00), LIFE22-IPC-ES-LIFE PYRENEES4CLIMA and the Institute for Water Research (IdRA) of the University of Barcelona.ReferencesBoone, A., Bellvert, J., Best, M., Brooke, J. K., Canut-Rocafort, G., Cuxart, J., ... & Zribi, M. (2025). The land surface interactions with the atmosphere over the iberian semi-arid environment (LIAISE) field campaign.Journal of the European Meteorological Society, 2, 100007.Udina, M., Peinó, E., Polls, F., Mercader, J., Guerrero, I., Valmassoi, A., ... & Bech, J. (2024). Irrigation impact on boundary layer and precipitation characteristics in Weather Research and Forecasting model simulations during LIAISE‐Quarterly Journal of the Royal Meteorological Society, 150(763), 3251-3273.

The role of precipitation in aerosol removal is crucial for accurate climate modelling and environmental management. The purpose of this study is to combine NASA's Micro-Pulse Lidar Network (MPLNET) lidar observations with micro-rain radar profiles to study the effects of precipitation on aerosol load in the atmosphere. The MPLNET lidar data are collected from NASA's Universitat Politecnica de Catalunya permanent observation site. Analysis of aerosol scattering before and after precipitation revealed significant changes backscattering vertical profile shape of data distribution. This study provides quantitative insights into the influence of precipitation on atmospheric aerosols and demonstrates the impact of rain by studying changes in backscatter profiles. The results of this research will make a significant contribution to the field of atmospheric science, particularly in enhancing our understanding of the interaction of aerosols, clouds and precipitation.

This study, conducted in the framework of the LIAISE field campaign in NE Spain (May–September 2021), investigates how near-surface relative humidity influences early-stage rainfall characteristics when precipitation is most affected by temperature and relative humidity before rainfall onset. Two instrumented sites were examined, using disdrometers, Micro Rain Radar (MRR), C-band weather radar data, and automatic weather stations. Rainfall events were first classified as stratiform or convective using weather radar data based on a texture analysis of the reflectivity field. Then, only stratiform events were selected and further classified into dry and moist categories according to the upper and lower terciles of near-surface (2 m) relative humidity at the rainfall onset (dry < 54%; moist > 72%). Results show that during dry events, the time delay between the detection of precipitation at ~750 m above ground level (AGL) (by MRR or C-band radar) and its arrival at the surface (measured by the disdrometer) is consistently longer than during moist events, indicating possible evaporation of raindrops during their descent. Surface drop size distributions also differ: dry cases have generally fewer small drops (with diameters < 0.8 mm) but relatively more large drops, leading to higher radar reflectivity values despite similar surface rainfall amounts. However, reflectivity observed aloft by C-band radar and MRR does not present the dependence on relative humidity found at ground level. Findings reported here increase our understanding of the impact of low-level conditions on precipitation characteristics and microphysical associated processes and may contribute to improve correction schemes in operational weather radar quantitative precipitation estimates.

Satellite-based precipitation products play a crucial role in providing global, continuous, and reliable estimates of rainfall, essential for understanding and managing Earth's water cycle. This study evaluates the accuracy of three H SAF satellite products (H61B, H64, and H68) and compares their performance with IMERG V06B (Early and Late Runs) products in detecting and estimating extreme precipitation events in the western Mediterranean region. The analysis is based on hourly and daily rainfall data collected from 186 rain gauges in Catalonia (NE Iberian Peninsula), using a point-to-pixel approach. The results show that satellite estimates tend to overestimate low precipitation accumulations (less than 2 mm and 10 mm in one hour and one day respectively), with this overestimation being more evident in the H68 product at the hourly scale and in IMERG Late at the daily scale. However, all products show a substantial decline in accuracy for higher precipitation amounts, particularly when they exceed 10 mm in one hour and 30 mm in one day (relative errors up to-60% and critical success index less than 33% in all cases). Despite its biases, IMERG Late product emerged as the most reliable for detecting substantial rainfall accumulations. Additionally, this analysis examined the relationship between the microphysical properties of the precipitating cloud top and estimated or observed surface precipitation. Accurate precipitation estimates from the satellite products were consistently associated with high values of Cloud Optical Thickness (COT) and Ice Water Path (IWP), while false alarms were often linked to low values of these variables. As expected, the results indicated a poorer performance in estimating precipitation associated with warm clouds. Additionally, these findings highlight the importance of integrating cloud optical and microphysical properties into retrieval algorithms to improve satellite precipitation estimates.

Agricultural areas in semi-arid regions modify low-level atmospheric conditions through changes in heat and moisture surface fluxes and enhanced evapotranspiration. This study aims to investigate the influence of near-ground-level relative humidity (RH) on local precipitation characteristics in a relatively flat, mid-latitude, semi-arid agricultural region, divided into a rainfed and an irrigated area with high evapotranspiration contrast in summer. The region was selected for the Land Surface Interactions with the Atmosphere over the Iberian Semi-Arid Environment (LIAISE) international field campaign in 2021 and here is studied using Automatic Weather Station observations and C-band weather radar data covering six years. Summer RH records show clear contrasts between irrigated and non-irrigated areas, unlike rain gauge and radar-derived rainfall, which do not exhibit substantial differences. A closer analysis indicates that RH differences between irrigated and non-irrigated areas before rainfall tend to diminish for several hours after the rainfall onset. This suggests that the presence of rainfall is temporally more important than whether the terrain is irrigated or not. Examination of radar reflectivity (Z) profiles considered convective and non-convective cases averaged during the first 30 and 180 min from the precipitation onset. Results indicated a dependence on ground-level RH for convective cases, leading to higher Z values with higher RH, clearer for the first 30 min averaged profiles. Finally, a linear relation was found between the lowest 1 km radar Z value and collocated RH for the first 30 min period of convective precipitation, increasing Z with RH. These results point out that, despite no differences in precipitation amounts found over contiguous irrigated and non-irrigated areas, there is a local impact of low-level moisture on convective rainfall.

One of the greatest challenges facing environmental science is to better understand the impacts of predicted future changes in the terrestrial hydrological cycle. It has been recognized that human activities play a key role and must therefore be considered in future climate simulations. The representation of anthropization in land surface schemes within global earth system models is at a relatively nascent stage and must be improved for more accurate future projections of water resources. The understanding of the impact of anthropogenic processes has been hampered by the lack of consistent and extensive observations. Here, we present the Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) project field campaign which brought together ground-based (surface energy budget estimated at 7 sites, 269 radio soundings made at 2 sites and multiple remote sensing instruments for profiling the lower atmosphere), airborne measurements (3 airplanes and numerous drones measuring surface and atmospheric properties) and satellite data (to derive estimates of irrigation timing, soil moisture, evapotranspiration and surface temperature) to improve our understanding of key natural and anthropogenic land processes and boundary layer feedbacks. The study area is in the Ebro basin of northeastern Spain in a hot, dry Mediterranean climate, with a sharp demarcation between a vast intensively irrigated region and a much drier rainfed zone to the east. Analysis of the observations reveal strong surface heterogeneities of evapotranspiration within the irrigated zone (differences upwards of approximately 7 mm day-1 between fields), linked to the crop type, vegetation phenology and soil moisture, all of which were modulated by irrigation. The significant surface flux differences between the irrigated and rainfed zones were found to result in strongly contrasting atmospheric boundary layer properties (between 2 supersites​ separated by 14 km) extending upwards through the lowest several km of the atmosphere.

This study investigates the effects of sea surface temperature (SST) updating strategy in the Weather Research and Forecasting (WRF) model during a heatwave event over the Northwestern Mediterranean Sea in July 2019. The MM5 revised Surface Layer and Yonsei University Planetary Boundary Layer (PBL) schemes were used and wind field at 10 m, air potential temperature, surface fluxes, and planetary boundary layer height were examined. Generally, the greatest impacts over the sea were observed within 20 km of the shoreline. We found an underestimation of the modeled SST in non-updated SST simulations during the heatwave episode that was propagated into the atmosphere, leading to a cold bias of 2-m potential temperature up to 2.5 K onshore. In heatwave conditions the most common surface layer stability class was very unstable, and its frequency increased when the SST was updated, particularly near the coast, revealing that SST updating leads to greater dominance of thermal turbulent mixing of surface fluxes in the heatwave period studied. Sensible and latent heat fluxes across stability regimes were analyzed, and latent heat flux showed greater sensitivity to SST updating and the highest magnitudes. However, PBL height variations between SST-updated and non-updated simulations presented a greater sensitivity to sensible heat flux. On average, during the heatwave period, the planetary boundary layer height in simulations with updated SST increased by 75 m onshore, compared to a smaller increase of 26 m offshore, which highlights the greater sensitivity in the onshore region and their impact on vertical modeled profiles. These results emphasize the importance of an accurate representation of boundary layer conditions on numerical weather prediction models, as well as illustrating the nonlinear behavior on the surface layer and PBL scheme, particularly important under heatwaves.

Nowadays, atmospheric pollution is one of the most relevant environmental issues. Some pollutants such as fine particulate matter with a diameter of 10 μm or less (PM10) have a considerable impact on human health. In Catalonia (NE Spain), Saharan Dust Intrusions are a major source of PM10. In recent decades, a positive trend of these intrusion episodes has been detected in the NW Mediterranean basin. Moreover, precipitation plays a role in pollutant scavenging processes.In our work, we make an analysis of the changes in PM10 concentrations with the precipitation episodes. Moreover, we put a special emphasis in the precipitation episodes which happen simultaneously with a Saharan Dust Intrusion. In consequence, we have analysed PM10 concentration data from the Catalan Network for Pollution Control and Prevention and precipitation data from Automatic Weather Stations Network of the Meteorological Service of Catalonia. We use data from four measurement points in Catalonia (Montsec Observatory, Fabra Observatory, Vic and Sort) which are the unique points with precipitation and PM10 measurement instruments at the same location. Dataset contains 4-year data from 2019 to 2022. Specifically, we evaluate how daily mean PM10 concentration values for all the days in the dataset change in comparison to the values of the same variable for the previous day. Moreover, we do a separate analysis for days with observed precipitation (wet days) and days without precipitation (dry days). Also, we perform the analysis for days with Saharan Dust Intrusion and days without Saharan Dust Intrusion. Furthermore, we filter daily PM10 concentration changes for different absolute values of this daily variation to see the differences between great and small changes of daily PM10 concentration. To our knowledge, this is the first study of these characteristics in this region of study.In general, we observe a decrease of daily PM10 mean concentration levels with precipitation in approximately 60% of the days. This percentage increases to 80% for daily changes of PM10 concentration higher than 10 µg m-3. In wet days with Saharan Dust Intrusion, daily PM10 mean concentration decreases only in 50% of the cases independently of the absolute value of PM10 concentration variation. However, in wet days without Saharan Dust Intrusion, daily PM10 mean concentration decreases approximately in 60% of the cases. This percentage grows up to 90% if we only consider changes of PM10 concentration higher than 10 µg m-3. In consequence, Saharan Dust Intrusions clearly interfere with the usual pollutant precipitation scavenging processes. In addition, we find that scavenging processes are more effective above a certain PM10 concentration variation threshold. This study was performed in the framework of the project "Towards a climate resilient cross-border mountain community in the Pyrenees (LIFE22-IPC-ES-LIFE PYRENEES4CLIMA)".

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.

Tornadoes are the meteorological phenomenon capable of producing the most intense surface winds in the Earth, sometimes exceeding 100 m s-1. Tornado wind speed can rarely be measured in-situ using anemometers and radar. Nevertheless, it can be estimated through an analysis of the observed damage, especially when performing in-situ damage surveys (Rodríguez et al., 2020). The Fujita (F) scale (Fujita, 1971), which relates damage on buildings and forest with wind speed, was proposed with the aim of assessing tornado intensity. In the early 2000s was revised by the Texas Tech University proposing the Enhanced Fujita (EF) scale (WSEC, 2006). Both F and EF scales are broadly used, although it is hampering to apply them out of the USA because most damage indicators are related to typical building structures of that country, which are significantly different from those which are common in other regions.Recently, it has been proposed the International Fujita (IF) scale, which has been developed by several contributors from European universities and meteorological services coordinated by the European Severe Storm Laboratory (ESSL, 2023). IF scale considers a large variety of constructive structures and its sturdiness. This allows carrying out a detailed wind speed estimation based on damage surveys.In this work we revisit the 122 tornadoes included in the Catalonia tornado database 2000-2023 (NE Iberian Peninsula) with the aim of classifying events according to the new IF scale. We analyse and discuss spatial and temporally the results. Moreover, we compare them with previous classifications performed using F and EF scales, which showed that 92% of tornadoes reported in the region were weak (EF0 or EF1), whereas 8% were significant (EF2 or stronger) (Rodríguez et al., 2021). This study is partly supported by project PID2021-124253OB-I00. ReferencesESSL, 2023. The International Fujita (IF) Scale for tornado and wind damage assessments. European Severe Storm Laboratory, Wessling, Germany. https://www.essl.org/cms/wp-content/uploads/IF-scale_v1.0d.pdfFujita T.T., 1971. Proposed characterization of tornadoes and hurricanes by area and intensity. SMRP Research Paper, 91: 48.Rodríguez O., Bech J., Soriano J.D., Gutiérrez D., Castán S., 2020. A methodology to conduct wind damage field surveys for high-impact weather events of convective origin. Nat. Hazards Earth Syst. Sci., 20 (5): 1513-1531. https://doi.org/10.5194/nhess-20-1513-2020Rodríguez O., Bech J., Arús J., Castán S., Figuerola F., Rigo T., 2021. An overview of tornado and waterspout events in Catalonia (2000–2019). Atmos. Res., 250: 105415, https://doi.org/10.1016/j.atmosres.2020.105415WSEC, 2006. A Recommendation for an Enhanced Fujita Scale (EF-scale). Wind Science and Engineering Center (Texas Tech University), Lubbock, Texas, USA. https://www.depts.ttu.edu/nwi/Pubs/EnhancedFujitaScale/EFScale.pdf

Sub-estimation of weather radar quantitative precipitation estimates (QPE) is often attributed to the classical mechanism of precipitation evaporation below the cloud layer. The aim of this study is to investigate the potential impact of evaporation on radar reflectivity profiles using data from co-located automatic weather stations (AWS), which furnish ground-level measurements of air temperature, pressure, and relative humidity. The study is based on a six-year observational dataset from an area characterized by intense agricultural activity and divided into two sub-areas: an irrigated area and a rainfed area, separated by an artificial channel. The research is carried out over The Land Surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) domain, in the eastern Ebro valley in Catalonia (NE Spain) using C-band weather radar observations and AWS data from the Meteorological Service of Catalonia.Although the analysis revealed clear differences in average ground-level temperature and humidity between the irrigated and non-irrigated areas in dry days during the warm season, no clear differences were found on average precipitation frequency, intensity, amount and convective fraction between the two sub-areas. A more detailed study, specifically focusing on reflectivity profiles occurring during the first 30 minutes of rain following a 24-hour dry period, was conducted to examine cases prone to rainfall evaporation. The results indicated that after a 30-minute period of the rainfall onset, ground-level AWS temperature and relative humidity of both irrigated and rainfed areas -which were different before rainfall- tended to converge indicating that during rainfall ground level conditions are quickly homogenized. Finally, for this 30 first minutes and specific conditions, radar reflectivity observations at 1 km height did exhibit a statistically significant correlation with ground-level relative humidity for convective cases, irrespective of the sub area (irrigated or rainfed) considered. These results contribute to enhance our understanding of possible evaporation effects on weather radar QPE and may serve as a basis for the future development of an evaporation correction method. This study was supported by projects RTI2018-098693-B-C32 and PID2021-124253OB-I00.

The Land Surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) campaign examined the impact of anthropization on the water cycle in terms of land-atmosphere-hydrology interactions. The objective of this study is to assess the effects of irrigation on the atmosphere and on precipitation in Weather Research and Forecasting model simulations during the LIAISE special observation period in July 2021. Comparisons between simulations and observations show better verification scores for air temperature, humidity, and wind speed and direction when the model included the irrigation parametrization, improving the model warm and dry bias at 2 m over irrigated areas. Other changes found are the weakening of the sea breeze circulation and a more realistic surface energy partitioning representation. The boundary-layer height is lowered in the vicinity of irrigated areas, causing a decrease in the lifting condensation level and the level of free convection, which induce increases in convective available potential energy and convective inhibition. Precipitation differences between simulations become relevant for smaller areas, close to the irrigated land. When convection is parametrized, simulations including irrigation tend to produce a decrease in rainfall (negative feedback), whereas convection-permitting simulations produce an increase (positive feedback), although the latter underestimates substantially the observed precipitation field. In addition, irrigation activation decreases the areas exceeding moderate hourly precipitation intensities in all simulations. There is a local impact of irrigated land on model-resolved precipitation accumulations and intensities, although including the irrigation parametrization did not improve the representation of the observed precipitation field, as probably the precipitation systems during the LIAISE special observation period in July 2021 were mostly driven by larger scale perturbations or mesoscale systems, more than by local processes. Results reported here not only contribute to enhance our understanding of irrigation effects upon precipitation but also demonstrate the need to include irrigation parametrizations in numerical forecasts to overcome the biases found. Including the effect of irrigation in the Weather Research and Forecasting model improves warm and dry biases at 2 m according to surface stations during LIAISE-2021. A general deceleration of the flow is produced when including irrigation, especially in the sea breeze front. Precipitation differences between simulations become relevant for smaller areas. When convection is parametrized, simulations that include irrigation tend to produce a decrease in the total rainfall and a decrease in the areas exceeding moderate hourly precipitation intensities. image

Air pollution is a great health concern for the governments, but also for the population, especially in urban environments. Particulate matter (PM) levels are regulated by European standards and are therefore continuously monitored by official measurement networks with precision instruments that are regularly calibrated. Although citizens often have access to open data, these networks are not usually dense. During the last years, low-cost sensors for measuring air quality have become popular, allowing the establishment of citizen science networks in which the population takes an active attitude in capturing measurements.In the framework of the European project I-CHANGE, the Barcelona Living Lab on Extreme Events has used eight Smart Citizen Kits of the company Fablab, with different low-cost sensors including the Plantower PMS5003 that estimates the concentration of PM10, PM2.5, PM1 (referring the number to the maximum radius of the measured particles, in micrometers). The sensors were initially installed alongside an official instrument GRIMM EDM 180 providing PM10 and PM2.5 for 3 weeks, to intercompare the ability of these devices to measure PM. These sensors together with low-cost weather stations have been distributed in different schools in Barcelona, in the context of a citizen science campaign with a double objective: to analyze the use of this type of sensors to have more detailed information on pollution in areas of Barcelona with different characteristics and to raise awareness in the school community and promote changes in habits in response to the European Green Pact.The results of the intercomparison show that the instruments have a good performance for PM2.5 estimation, with an average determination coefficient (R2) of 0.84 with the official instrument, when comparing 10-minute averages. On the other hand, the instruments have worse quality in the estimation of PM10 (R2=0.64) as could be seen during the Saharan dust intrusion that affected the city. The laser-based measurement system does not allow a good characterization of coarse particles. Despite these differences with the official data, the agreement among low-cost sensors was good (R2 higher than 0.95 for PM10 and PM2.5), so the variations detected when displayed separately can be relied upon. During the time they have been located in schools, starting in January 2023 the longest series, they have allowed to monitor PM concentrations in different areas of the city. The comparison of PM evolution between official instruments and low-cost sensors during high concentration events have shown that the latter can have an informative and pedagogical role in raising public awareness on air quality.This study is supported by the project “Individual Change of HAbits Needed for Green European transition (I-CHANGE)”. I-CHANGE has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement 101037193.

In the last decade, substantial improvements have been achieved in quantitative satellite precipitation estimates, which are essential for a wide range of applications. In this study, we evaluated the performance of Integrated Multi-satellitE Retrievals for GPM (IMERG V06B) at the sub-daily and daily scales. Ten years of half-hourly precipitation records aggregated at different sub-daily periods were evaluated over a region in the Western Mediterranean. The analysis at the half-hourly scale examined the contribution of passive microwave (PMW) and infrared (IR) sources in IMERG estimates, as well as the relationship between various microphysical cloud properties using Cloud Microphysics (CMIC–NWC SAF) data. The results show the following: (1) a marked tendency to underestimate precipitation compared to rain gauges which increases with rainfall intensity and temporal resolution, (2) a weaker negative bias for retrievals with PMW data, (3) an increased bias when filling PMW gaps by including IR information, and (4) an improved performance in the presence of precipitating ice clouds compared to warm and mixed-phase clouds. This work contributes to the understanding of the factors affecting satellite estimates of extreme precipitation. Their relationship with the microphysical characteristics of clouds generates added value for further downstream applications and users’ decision making.

July 2019 in Catalonia, northeastern Spain, was an anomalously warm month marked by an irregular precipitation pattern, both spatially and temporally. Throughout this period, some discrepancies between WRF operational forecasts and observations were detected, which might stem from the lack of the sea surface temperature (SST) updating in the WRF model configuration. To study the SST-updating effects on WRF v4.5, two simulations were performed using ERA5 as initial and boundary conditions, the first one without updating SST and the second one updating it. Specifically, the study focused on two distinct periods of July 2019: days 8-10, characterized by storms over the Pyrenees (hereafter STORM), and days 24-26, characterized by a heatwave (hereafter HEATW). The objectives of this study are 1) to assess the SST updating impacts on WRF model, particularly on the surface layer and planetary boundary layer (PBL) parametrizations, and 2) to explore the differential behavior of the WRF model between storm and heatwave conditions. Results show that changes in SST modifies surface layer parameterization through surface fluxes, with the latent heat flux being more sensitive than the sensible heat flux. These fluxes impacted the stability regimes through the modification of the Monin-Obukhov length and the stability functions. During STORM period the atmosphere tends towards neutrality when updating SST, hence is dominated by wind shear turbulence, whereas on HEATW period the SST updating leads to very unstable conditions, dominated by buoyancy.Regarding the PBL parameterization, the SST updating leads to an increase of the PBL height during STORM and a decrease during HEATW, with a greater absolute variation observed during the latter period. Averaged potential temperature vertical profiles at 00 UTC reveal greater variability during storm conditions, especially at higher levels (500-1000 m), whereas during heatwave situations, the surface mixing layer is discernible up to ~80 m, corresponding to the maximum mixing height. Furthermore, during HEATW the averaged potential temperature profile revealed the maximum differences between the updated and non-updated simulations at the surface, with differences decreasing with height. Conversely, during STORM, the higher differences between profiles were located a few tens of meters above the surface.These findings show the significance of SST updating in operational forecasting during July 2019. Specifically, during the period of storm, the SST updating presents a greater impact on meteorological variables, particularly on air potential temperature, compared to the period of heatwave. These results are specific to July 2019 over the northwestern Mediterranean Sea, and further investigation is needed to assess their applicability in different time periods with similar meteorological conditions. Despite that, the study helps to a better understanding of the sensitivity of the WRF model to SST changes and the need of a proper SST representation.

Summer heatwaves and extended dry spells create optimal meteorological conditions for occasional dry thunderstorms to produce simultaneous lightning-ignited wildfires (LIW). Concurrent ignitions put a significant burden on the firefighting's initial attack, potentially allowing incipient LIWs to escape and grow into large fires. While we can reasonably forecast lightning activity, predicting dry thunderstorm conditions and potential LIW outbreaks remains challenging. In the present study, we analyze the meteorological factors associated with a LIW outbreak that took place in Catalonia on 15 June 2022, with 22 LIW reported in three consecutive days. The fire hazard was high, but not different from past LIW episodes. ERA5-derived indices related to low-level moisture showed extreme values compared to previous studies. Atmospheric conditions with an elevated lifting condensation level coupled with the synoptic framework were set for the formation of dry thunderstorms. Radar reflectivity profiles revealed sub-cloud evaporation, and rain-gauge records corroborated the occurrence of dry lightning. In the context of global warming, we expect an increase in the frequency of LIW outbreaks in the European Mediterranean region due to an increase in lightning-ignition efficiency, which refers to the average chance of fire per lightning stroke.

Air pollution is currently a major environmental issue to human health and natural ecosystems so improving air quality monitoring techniques, traditionally based on ground-based observation networks, is essential. Satellite remote sensing of air pollutants has made significant strides in recent years and now serves as a complementary data source alongside ground sensors. For example, different studies have explored the relationship between satellite-derived NO2 total column data and ground-level concentration but none of them focused on complex terrain areas. The aim of this work is to evaluate the feasibility of using NO2 column data from the Sentinel 5P satellite over complex terrain such as the Pyrenees Mountain area covering France, Spain and Andorra to estimate ground level values. For this purpose, a number of models considering the separation of temporal average and fluctuations are considered for both satellite and ground sensor data. The primary objective of these models is to enhance the signal-to-noise ratio. Initially, the periodicities are identified and subtracted from the original data, resulting in a residual series. These residual series are then filtered to eliminate noise while retaining the significant events. Finally, these new series are combined with the previously identified periodicity. Preliminary results over Andorra show that our models can enhance Pearson's correlation between the temporal series of the satellite and ground sensor, improving it from 0.415 to 0.650. In addition, it has been found that the NO2 annual cycle in Andorra can be detected with a correlation of 0.950 between the model and the ground sensor NO2 series. Furthermore, a weekly cycle during winter has been detected in the Sentinel NO2 series too. These findings suggest that satellite estimates can identify days with high risk of exceeding NO2 ground level thresholds, enabling the creation of risk maps for areas lacking ground sensors. Such results could profoundly impact air quality monitoring in major towns located in valleys of mountain areas. Peak concentrations that deviate from average cycles have also been quantified. These deviations will be compared with other locations characterized by simpler topography to gain a deeper understanding of the limitations of satellite estimates. Subsequently, the next phase involves integrating these models into Machine Learning Algorithms to expand the application of Sentinel 5P data to complex terrain areas. This study is supported by the project “Towards a climate resilient cross-border mountain community in the Pyrenees (LIFE-SIP PYRENEES4CLIMA)”.