Meteorological Extremes Research Articles

Applicability of different extreme weather datasets for assessing indoor overheating risks of residential buildings in a subtropical high-density city

A failure to consider extreme weather conditions in building design can lead to poor resilience and low passive survivability of buildings. Several approaches exist to construct extreme weather files for building performance assessments. Since literature comparing such extreme weather datasets is limited, this study aims to examine the applicability and limitations of the Summer Reference Year (SRY), Typical Hot Year-Event (THY-E), Typical Hot Year-Intensity (THY-I), Extreme Meteorological Year (XMY), and Typical Meteorological Year (TMY) in assessing indoor overheating risks of residential buildings, especially in a subtropical high-density living environment like Hong Kong. By comparing the simulated temperature with on-site measurements on different summer days, building physical parameters of six typical residential archetypes are calibrated in EnergyPlus. Their indoor overheating risks are then evaluated by two overheating criteria: the static extreme and adaptive thermal comfort thresholds. Results reveal that using the THY-I can generally examine the severest daytime overheating, but may fail to evaluate the maximum heat intensity of well-shaded buildings. The longest duration of daytime overheating is observed when using the THY-E, and the severest and longest nighttime overheating are found using the XMY. By contrast, using the SRY is unsuitable for assessing nighttime overheating risks. This study suggests advantages of using a combination of different extreme weather datasets, e.g., the XMY with the THYs, to assess overheating risks in high-density settings over the use of a single weather dataset. Furthermore, the building type with balconies and openable windows coated with low-e consistently demonstrates better performance than the other types.

Evaluating a land surface model at a water-limited site: implications for land surface contributions to droughts and heatwaves

Abstract. Land surface models underpin coupled climate model projections of droughts and heatwaves. However, the lack of simultaneous observations of individual components of evapotranspiration, concurrent with root-zone soil moisture, has limited previous model evaluations. Here, we use a comprehensive set of observations from a water-limited site in southeastern Australia including both evapotranspiration and soil moisture to a depth of 4.5 m to evaluate the Community Atmosphere-Biosphere Land Exchange (CABLE) land surface model. We demonstrate that alternative process representations within CABLE had the capacity to improve simulated evapotranspiration, but not necessarily soil moisture dynamics–highlighting problems of model evaluations against water fluxes alone. Our best simulation was achieved by resolving a soil evaporation bias, using a more realistic initialisation of the groundwater aquifer state and higher vertical soil resolution informed by observed soil properties, and further calibrating soil hydraulic conductivity. Despite these improvements, the role of the empirical soil moisture stress function in influencing the simulated water fluxes remained important: using a site-calibrated function reduced the soil water stress on plants by 36 % during drought and 23 % at other times. These changes in CABLE not only improve the seasonal cycle of evapotranspiration but also affect the latent and sensible heat fluxes during droughts and heatwaves. The range of parameterisations tested led to differences of ∼150 W m−2 in the simulated latent heat flux during a heatwave, implying a strong impact of parameterisations on the capacity for evaporative cooling and feedbacks to the boundary layer (when coupled). Overall, our results highlight the opportunity to advance the capability of land surface models to capture water cycle processes, particularly during meteorological extremes, when sufficient observations of both evapotranspiration fluxes and soil moisture profiles are available.

Seasonal Variations of Fine Particulate Matter and Mortality Rate in Seoul, Korea with a Focus on the Short-Term Impact of Meteorological Extremes on Human Health

Rapid industrialization of Korea’s economy has brought with it environmental pollution that threatens human health. Among various other pollutants, ambient fine particulate matter known to endanger human health often exceeds air quality standards in Seoul, South Korea’s capital. The goal of this research is to find the impact of meteorological extremes and particle levels on human health. The analysis was conducted using hourly air pollutant concentrations, meteorological variables, and the daily mortality from cerebrovascular disease. Results show that the effect of fine particulate matter on mortality from cerebrovascular disease was more noticeable during meteorological extremes. The linkage between extreme weather conditions and mortality was more apparent in winter than in summer. Comprehensive studies of various causes of diseases should be continued to more accurately analyze the effects of fine particulate matter on human health and meteorological extremes, and to further minimize the public health impact of air pollution and meteorological conditions.

Imputation using information fusion technique for sensor generated incomplete data with high missing gap

Reliable data is crucial to furnish Cyber Physical Systems (CPS) utilities with quality. Connection error, meteorological extremes, sensing error, human error, etc. are reasons for incomplete data in CPS. Missing data is a leading challenge that confines development of CPS and applications. In this paper, we present Ratio-Based Imputation (RBI) algorithm to handle large missing gap based on combination of data fusion and data mining approach. Imputation is performed by data mining technique whereas analysis is done by data fusion technique. Different simulated datasets were generated from real dataset with random deletion of missing value with range from 6% to 25% where highest missing gap consists of 21% of missing data. Various performance indicators are used to compare accuracy of imputation of missing value. Results demonstrated that proposed method is effective and efficient as results enhanced on average of 26% and 28% based on root mean square error and mean absolute error on highly correlated datasets compared to a popular algorithm. All performance indicators accede to appraise proposed method performs better than existing multiple imputation method we compared. Projected imputation procedure exhibited better accuracy, data structure, and low error under various circumstances.

COVID-Induced Investor Sentiments and Market Reaction under Extreme Meteorological Conditions: Evidence from Clean Energy Sector of Asia-Pacific

The unprecedented global economic and social crisis caused by the coronavirus outbreak has not spared the energy sector. Using a dynamic model, we investigated the effect of COVID-19 cases on investor sentiments and stock returns of clean energy in the Asian-Pacific region. The results show that coronavirus cases negatively affect stock returns using investor sentiments as a transmission channel. We also find a negative effect of air pollution on stock returns. Since COVID-19 restricted trade and plummeted the oil prices, economies relied on non-renewable sources to meet energy demands. Nevertheless, the investor’s optimism and high sentiment level may deteriorate this link. On the other hand, we do not find any significant effect of low-high temperature on either investor sentiments or clean energy stock returns. Clean energy stocks were viewed as more sustainable and less vulnerable to external shocks, however, the fear and pessimism among investors induced by corona-virus are spilled over the renewable energy sector.

Emergence of urban clustering among U.S. cities under environmental stressors

Cities are the hotspots of global human–environment interactions, and their sustainable development requires proactive strategies to mitigate and adapt to emergent environmental issues. Nevertheless, most of the existing studies and strategies are based on specific (and often singular) environmental processes, and their efficacy is largely undermined by their heavy dependence on locality. Here we present a novel modeling framework for urban studies to capture spatial connectivity and teleconnection among cities in response to different environmental stressors. For illustration, a generic message-passing-based algorithm is used to identify spatial structures among U.S. cities. Urban structures are analyzed under two types of environmental stressors, i.e., extreme heat and air pollution, based on remotely sensed land surface temperature data during short-term heat wave events and a yearlong remotely sensed aerosol optical depth dataset, respectively. Results show that U.S. cities are clustered as locally and regionally connected groups, while the hub–periphery organization manifest via environmental similarity and atmospheric transport under both event-scale meteorological extremes and long-term environmental stressors. The physics-driven urban agglomeration reveals that cities are multilevel interconnected complex systems rather than isolated entities. The proposed framework provides a new pathway to shift goal- or process-based urban studies to system-based global ones.

Assessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analogues

Abstract. Natural risk studies such as flood risk assessments require long series of weather variables. As an alternative to observed series, which have a limited length, these data can be provided by weather generators. Among the large variety of existing ones, resampling methods based on analogues have the advantage of guaranteeing the physical consistency between local weather variables at each time step. However, they cannot generate values of predictands exceeding the range of observed values. Moreover, the length of the simulated series is typically limited to the length of the synoptic meteorological records used to characterize the large-scale atmospheric configuration of the generation day. To overcome these limitations, the stochastic weather generator proposed in this study combines two sampling approaches based on atmospheric analogues: (1) a synoptic weather generator in a first step, which recombines days of the 20th century to generate a 1000-year sequence of new atmospheric trajectories, and (2) a stochastic downscaling model in a second step applied to these atmospheric trajectories, in order to simulate long time series of daily regional precipitation and temperature. The method is applied to daily time series of mean areal precipitation and temperature in Switzerland. It is shown that the climatological characteristics of observed precipitation and temperature are adequately reproduced. It also improves the reproduction of extreme precipitation values, overcoming previous limitations of standard analogue-based weather generators.

Influence of growing season temperature and precipitation anomalies on crop yield in the southeastern United States

This study investigates the spatiotemporal relationships between growing season precipitation, maximum temperature, and minimum temperature anomalies on yield for corn (Zea mays), soybean (Glycine max), cotton (Gossypium), peanut (arachis hypogaea), and sweet potato (Ipomoea batatas) crops in the southeastern United States (SEUS). Detrended county-level yield data (1981–2018) were analyzed alongside spatially derived growing season (May-Oct) climate anomalies. Results reveal that the relationships between climate anomalies and crop yield differ considerably across the SEUS based on the crop type and timing of meteorological extremes. Aligning with previous findings, surface crops in the region suffer considerable declines as a result of higher than normal maximum temperatures during the growing season, with the most significant losses occurring during the months of July and August when daytime temperatures frequently exceed ideal growing conditions. While the association is weaker, higher than normal minimum temperatures during critical crop development stages were also found to lead to significant declines in crop productivity. Notably, although drought conditions result in negative departures from expected yield, the findings of this study highlight that excess moisture in the latter part of the growing season (Sep-Oct) can be equally damaging for certain regional crops, including peanuts and sweet potatoes. The results of this study underscore the need for further research on the impact of climatic variability on regional and specialty crops in the era of anthropogenic climate change.

Can Crop Models Identify Critical Gaps in Genetics, Environment, and Management Interactions?

Increasing food demand under climate change constraints may challenge and strain agricultural systems. The use of crop models to assess genotypes performance across diverse target environments and management practices, i.e., the genetic × environment × management interaction (GEMI), can help understand suitability of genotype and agronomic practices, and possibly accelerate turnaround in plant breeding programs. However, the readiness of models to support these tasks can be debated. In this article, we point out modeling and data limitations and argue the need for evaluation and improvement of relevant process algorithms as well as model convergence. Under conditions suitable for plant growth, without meteorological extremes or soil limitation to root exploration, models can simulate resource capture, growth, and yield with relative ease. As stresses accumulate, the plant species‐ and genotype-specific attributes and their interactions with the soil and atmospheric environment generate a large range of responses, including conditions where resources become so limiting as to make yields very low. The space in between high and low yields is where most rainfed production occurs, and where the current model and user skill at representing GEMI varies. We also review studies comparing the performance of a large number of crop models and the lessons learned. The overall message is that improvement of models appears as a necessary condition for progress, and perhaps relevancy. Model ensembles help mitigate data input, model, and user-driven uncertainty for some but not all applications, sometimes at a very high cost. Successful model-based assessment of GEMI not only requires better crop models and knowledgeable users, but also a realistic representation of the environmental conditions of the landscape where crops are grown, which is not trivial given the 3D nature of water and nutrient transport. Models remain the best quantitative repository of our knowledge on crop functioning; they contain a narrative of plant, soil, and atmospheric functioning in computer language and train the mind to couple processes. But in our quest to tame GEMI, will they lead the way or just ride along history?

Northward establishment of the mediterranean mussel Mytilus galloprovincialis limited by changing climate

Throughout Europe the blue mussel Mytilus edulis is dominant in the north while the Mediterranean mussel Mytilus galloprovincialis prevails in the south. Studies from the 1970s to the late 2000′s documented the northward range expansion of M. galloprovincialis in Europe and predicted this trend to continue with climate change. The objectives of this study were to sample predominantly wild mussels (n = 1459) at twenty-four Irish intertidal sites over a seven year period and at three Welsh sites to investigate (a) the abundance and distribution of Mytilus spp., identified molecularly by polymerase chain reaction (PCR), and (b) compare with historical observations, at Irish sites where M. galloprovincialis was observed to be more abundant than M. edulis. Mussels were sampled more than once at certain sites to investigate if there was a temporal effect. The findings of this study indicated that M. edulis was consistently the most abundant species, followed by hybrids and M. galloprovincialis. At certain sites, hybrids were detected while M. galloprovincialis was absent. This finding may indicate transient M. galloprovinicialis populations or input from individuals subtidally. One factor that may be of importance was the anomalous cold winter “polar snaps” (2010 and 2011) that occurred during this study. In addition, heavy precipitation events and subsequent increased freshwater loading in bays and estuaries also occurred during this time period. Future warming climate scenarios have been predicted to facilitate the northwards establishment of M. galloprovincialis, however, nearshore meteorological extremes may have an impact in its larval settlement, establishment and subsequent reproductive output.

Lag in Hydrologic Recovery Following Extreme Meteorological Drought Events: Implications for Ecological Water Requirements

Hydrological regimes, being strongly impacted by climate change, play a vital role in maintaining the integrity of aquatic river habitats. We investigated lag in hydrologic recovery following extreme meteorological drought events, and we also discussed its implications in the assessment of ecological environment flow. We used monthly anomalies of three specific hydrometeorological variables (precipitation, streamflow, and baseflow) to identify drought, while we used the Chapman–Maxwell method (the CM filter) with recession constant calculated from Automatic Baseflow Identification Technique (ABIT) to separate baseflow. Results showed that: (i) Compared to the default recession parameter (α = 0.925), the CM filter with the ABIT estimate (α = 0.984) separated baseflow more accurately. (ii) Hydrological drought, resulting from meteorological drought, reflected the duration and intensity of meteorological drought; namely, longer meteorological drought periods resulted in longer hydrological drought periods. Interestingly, the time lag in streamflow and baseflow indicated that aquatic ecosystem habitat recovery also lagged behind meteorological drought. (iii) Assessing environmental flow by quantifying drought provided greater detail on hydrological regimes compared to abrupt changes, such as the increased hydrological periods and the different environment flows obtained. Taken together, our results indicated that the hydrological response in streamflow and baseflow (e.g., the time lag and the precipitation recovery rate (Pr)) played a vital role in the assessment of environmental flow.

Weather extremums in Siberia in 2019 and their connection with circulation of the atmosphere

According to the circulation seasons [Dzerdzeevsky et al., 1946], according to the website Weather News, extreme events in the Asian territory of Russia for first half of 2019 were considered: daily maximums and minimums of air temperature; maximum daily amplitudes of air temperature, daily maximums and monthly lows of atmospheric precipitation; daily maximums wind speeds, maximum and minimum of snow depths and dangerous natural processes. Their connection with the atmospheric circulation of the Northern Hemisphere has been shown [Atmospheric Circulation fluctuations site 18992018].
 In recent years, extreme situations are increasingly becoming apparent in the Asian part of Russia. This attracts the close attention of researchers [Latysheva et al., 2010; Zolina, Bulygina, 2016]. They try to identify the causes of what is happening [Vasilyev et al., 2018; Kononova, 2018; Kochugova, 2018; Tarabukina et al., 2018] and predict the future nature of extrema [Shkolnik et al., 2012]. It seemed interesting to analyze the extremes of the current year (more precisely, the first half of it), to show the real situation today.
 The majority of extremes, surpassed in the first half of 2019, refers to the 21st century. This means that extremity has been growing rapidly in recent years. The consequence of this is an increase in the frequency of occurrence of dangerous natural processes.
 The main source of information on daily meteorological extremes was Weather News [Meteonovosti.ru]. It briefly informed about fires and floods. More detailed information was taken from local sites [Amur.Info, Taiga.Info, Ulpress.ru, Social media news]. The character of atmospheric circulation was analyzed by classification [Dzerdzeevsky et al., 1946] using data from the site of the Atmospheric Circulation fluctuations.. for 1899 2018
 The most disastrous flood was June 24 - 29 in the Irkutsk region. It destroyed more than 10,000 residential homes in 98 settlements, 43 schools, kindergartens and hospitals. According to information on July 11, 25 people died.
 The strongest were fires in the Krasnoyarsk Territory and the Irkutsk Region in the third decade of July. Smoke from them reached the Ulyanovsk region.
 The conducted research allows to draw the following conclusions. The number of extremes of both air temperature and precipitation in the XXI century continues to grow. Negative extremes of precipitation in combination with positive extremes of air temperature lead to natural fires, positive extremes of precipitation - to catastrophic floods. The increasing frequency of atmospheric circulation contributes to an increase in the frequency of occurrence of those and others: an increase in the frequency of occurrence of blocking processes (arctic invasions resulting in the formation of a vast stationary anticyclone) and exits of southern cyclones.

Evaluation of WRF-CMAQ simulated climatological mean and extremes of fine particulate matter of the United States and its correlation with climate extremes

Fine particulate matter (PM2.5, with aerodynamic diameters < 2.5 μm) pollution is one of the most pervasive air quality problems facing the world. Reliable air quality modeling of PM2.5 is essential to future air quality projection, which serves as a critical source of information for policy-making. Although various evaluation methods have been suggested to assess the capability of air quality models in reproducing PM2.5, most of studies were focused on the mean behaviors of air quality models, with little emphasis on extreme conditions, which may be more crucial for human health and climate change. To address this need, we proposed an evaluation framework in this study to characterize both mean and extreme conditions of PM2.5 and applied it to the WRF-CMAQ simulations over contiguous United States for the period of 2001–2010. Results from statistical, spatiotemporal, and extreme quantile evaluation methods show consistent good performance of the model in the Eastern U.S. However, PM2.5 mean variations and extreme trends in the western U.S. are not well represented by the model attributable to the existence of complex terrains and active fire activities. In addition, the magnitude of decreasing trends for extreme events is smaller than that for the mean PM2.5. Strong correspondence is found between PM2.5 extremes and meteorological extremes that are associated with a stagnant condition. More extreme PM2.5 pollution episodes are expected in a warming climate, with rural regions and the western U.S. suffering the most. Our results highlight the urgency for proper forest management and joint-control of air quality and carbon emissions in order to combat extreme air pollution events in the future.

Impact of Barrier Breaching on Wetland Ecosystems under the Influence of Storm Surge, Sea-Level Rise and Freshwater Discharge

Coastal wetland ecosystems and biodiversity are susceptible to changes in salinity brought about by the local effects of climate change, meteorological extremes, coastal evolution and human intervention. This study investigates changes in the salinity of surface water and the associated impacts on back-barrier wetlands as a result of breaching of a barrier beach and under the compound action of different surge heights, accelerated sea-level rise (SLR), river discharge and rainfall. We show that barrier breaching can have significant effects in terms of vegetation die-back even without the occurrence of large storm surges or in the absence of SLR, and that rainfall alone is unlikely to be sufficient to mitigate increased salinity due to direct tidal flushing. Results demonstrate that an increase in sea level corresponding to the RCP8.5 scenario for year 2100 causes a greater impact in terms of reedbed loss than storm surges up to 2 m with no SLR. In mitigation of the consequent changes in wetland ecology, regulation of relatively small and continuous river discharge can be regarded as a strategy for the management of coastal back-barrier wetland habitats and for the maintenance of brackish ecosystems. As such, this study provides a tool for scoping the potential impacts of storms, climate change and alternative management strategies on existing wetland habitats and species.

The meteorological future is here

Rain Climatologists have predicted that anthropogenic climate change will cause more and more intense rainfall (along with other meteorological extremes), and it seems like that is occurring, but have precipitation patterns really changed enough to be objectively noticeable? Lehmann et al. present data showing that this impression does accurately reflect reality. They show that heavy rainfall events now occur more frequently than they did only 40 years ago in most of the world, except in Central Africa, where record-dry months have become more common. These trends are consistent with the changes in mean monthly precipitation expected to accompany climate change and should become more extreme in the future. Geophys. Res. Lett. 10.1029/2018GL079439 (2018).

Impact of Extreme Meteorological Events on Ozone in the Pearl River Delta, China

ABSTRACT Along with rapid economic development in the Pearl River Delta (PRD) region of China for the past two decades, ozone (O3) pollution has deteriorated significantly. Extreme meteorological events (EMEs), including heat wave (HW), atmospheric stagnation (AS), and temperature inversion (TI), exert significant impacts on O3. Base on observational O3 data and meteorological reanalysis data, we analyze the impact of EMEs on O3 during O3 season of April–October in 2006–2017 over the PRD. Statistical analysis indicates significant but spatially heterogeneous sensitivities of O3 to EMEs. AS poses the largest impact on O3 concentration over the PRD, resulting in 58% increase compared with normal days, while the increases by HW and TI are 28% and 14%, respectively. O3 pollution events are largely initiated by HW and AS which favor formation and build-up of O3, while O3 pollution events are maintained mostly by persistent AS and TI. HW poses higher impacts on northern and eastern PRD, while AS impacts more on central and western PRD. The effect of AS on O3 concentration is similar as 10 K temperature increase during non-AS days, while the effect of AS and TI on O3 exceedance is comparable with 6 K temperature increase during non-EMEs condition. O3 concentrations under different synoptic patterns are largely associated with the occurrence of AS, and Siberian high and the approaching of a tropical cyclone are the dominant synoptic patterns for EMEs impact on O3 and largely determines the long-term increasing trend of O3 concentration over the PRD. This study highlights the importance of establishing a location-specific O3 control strategy targeting on normal conditions and O3 pollution events separately. This study also provides scientific support to use EMEs forecast as an indicator to implement contingency O3 control in advance so as to maximize peak O3 reduction over the PRD.

Identifying the Ideal Number Components of the Bayesian Principal Component Analysis Model for Missing Daily Precipitation Data Treatment

The grassroots of the presence of missing precipitation data are due to the malfunction of instruments, error of recording and meteorological extremes. Consequently, an effective imputation algorithm is indeed much needed to provide a high quality complete time series in assessing the risk of occurrence of extreme precipitation tragedy. In order to overcome this issue, this study desired to investigate the effectiveness of various Q-components of the Bayesian Principal Component Analysis model associates with Variational Bayes algorithm (BPCAQ-VB) in missing daily precipitation data treatment, which the ideal number of Q-components is identified by using The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. The effectiveness of BPCAQ-VB algorithm in missing daily precipitation data treatment is evaluated by using four distinct precipitation time series, including two monitoring stations located in inland and coastal regions of Kuantan district, respectively. The analysis results rendered the BPCA5-VB is superior in missing daily precipitation data treatment for the coastal region time series compared to the single imputation algorithms proposed in previous studies. Contrarily, the single imputation algorithm is superior in missing daily precipitation data treatment for an inland region time series rather than the BPCAQ-VB algorithm.

A virtual thematic research environment for analysis of climate change and its consequences for Northern Eurasia: the current state of the art and perspectives

A virtual research environment aimed at analysis of climate change and its impact on Northern Eurasia based on climatic data archives and dedicated analytics embedded into a web-GIS called “Climate” is presented. An extended set of analytical procedures related to the analysis of climatic and meteorological extremes is described. An updated web portal structure is described to facilitate the system use by the general public, regional stakeholders, and decision makers and get the required information without using a tedious registration procedure.

Fenómenos climáticos extremos y sus efectos en el Caribe

Durante los primeros quince años de este siglo, los fenómenos meteorológicos extremos (ciclones tropicales, olas de calor, frentes fríos, incendios forestales, sequías, etc.) han mostrado una tendencia que va en aumento. El propósito principal de este artículo es efectuar un análisis de los fenómenos meteorológicos extremos (FME), con el objetivo de motivar a la reflexión a los tomadores de decisiones y al público en general sobre la necesidad de estar preparados para los posibles efectos de estos eventos Además, el artículo analiza el impacto de estos fenómenos en los diferentes aspectos del diario vivir, tales como: salud de la población con enfermedades como dengue, chikungunya, zika, irritación de ojos y piel, entre otros; así como sobre la economía del país. En el caso particular de la República Dominicana, enclavada en el centro del Caribe y su dependencia del turismo y la agricultura, el preparar planes de mitigación y adaptación con el propósito de ser más resilientes es una obligación del Estado, así como de los gobiernos municipales.

Assessment of Satellite and Radar Quantitative Precipitation Estimates for Real Time Monitoring of Meteorological Extremes Over the Southeast of the Iberian Peninsula

Quantitative Precipitation Estimates (QPEs) obtained from remote sensing or ground-based radars could complement or even be an alternative to rain gauge readings. However, to be used in operational applications, a validation process has to be carried out, usually by comparing their estimates with those of a rain gauges network. In this paper, the accuracy of three QPEs are evaluated for three extreme precipitation events in the last decade in the southeast of the Iberian Peninsula. The first QPE is PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Cloud Classification System) , a satellite-based QPE. The second and the third are QPEs from a meteorological radar with Doppler capabilities that works in the C band. Pixel-to-point comparisons are made between the values offered by the QPEs and those obtained by two networks of rain gauges. The results obtained indicate that all the QPEs were well below the rain gauge values in extreme rainfall time slots. There seems to be a weak linear association between the value of the discrepancies and the precipitation value of the QPEs. The main conclusion, assuming the information from the rain gauges as ground truth, is that neither PERSIANN-CCS nor radar, without empirical calibration, are acceptable QPEs for the real-time monitoring of meteorological extremes in the southeast of the Iberian Peninsula.