Mitigation plans to counteract overheating in urban areas need to be based on a thorough knowledge of the state of the thermal environment, most importantly on the presence of areas which consistently demonstrate higher or lower urban land surface temperatures (hereinafter referred to as “hot spots” or “cold spots”, respectively). The main objective of this research study is to develop a methodological approach for the recognition of thermal “hot spots” and “cold spots” in urban areas during summer; this is accomplished with (a) the combined use of high and medium spatial resolution satellite data (Landsat 8 and Terra-MODIS, respectively); (b) the downscaling of the Terra-MODIS satellite data so as to acquire spatial resolution similar to the Landsat one and at the same time take advantage of the high revisit time as compared to the respective one of Landsat (16 days); and (c) the application of a statistical clustering technique to recognize “hot spots” and “cold spots”. The methodological approach was applied as a case study for the urban area of Athens, Greece for a summer period. Results demonstrated the capacity of the methodological approach to recognize “hot spots” and “cold spots”, revealed a strong relationship between land use and “hot spots” and “cold spots”, and showed that the average land surface temperature (LST) difference between the “hot spots” and “cold spots” can reach 9.1 °K.

Land Surface Temperature (LST) is a key parameter for the estimation of urban fluxes as well as for the assessment of the presence and strength of the surface urban heat island (SUHI). In an urban environment, LST depends on the way the city has been planned and developed over time. To this end, the estimation of LST needs adequate spatial and temporal data at the urban scale, especially with respect to land cover/land use. The present study is divided in two parts: at first, satellite data from MODIS-Terra 8-day product (MOD11A2) were used for the analysis of an eighteen-year time series (2001–2017) of the LST spatial and temporal distribution in five major cities of the Mediterranean during the summer months. LST trends were retrieved and assessed for their statistical significance. Secondly, LST values and trends for each city were examined in relation to land cover characteristics and patterns in order to define the contribution of urban development and planning on LST; this information is important for the drafting of smart urbanization policies and measures. Results revealed (a) positive LST trends in the urban areas especially during nighttime ranging from +0.412 °K in Marseille to +0.923 °K in Cairo and (b) the SUHI has intensified during the last eighteen years especially during daytime in European Mediterranean cities, such as Rome (+0.332 °K) and Barcelona (+0.307 °K).

In recent years, an exceptional number of record-shattering temperature extremes have been observed, resulting in significant societal and environmental impacts. The Mediterranean region is particularly thermally vulnerable, frequently suffering from intense and severe heatwaves. Using daily temperature observations from 58 weather stations (NOAA Global Historical Climatology Network daily database) in the Mediterranean area, past heatwave episodes were initially detected. A daily LST time series was developed using Land Surface Temperature (LST) products from Moderate Resolution Imaging Spectroradiometer (MODIS) (Terra & Aqua satellites) for a 19-year period (2002–2020) at the station locations. LST anomalies were identified using percentile-based indices. It was found that remotely sensed-based LST presents the potential for understanding and monitoring heatwave events, as surface thermal anomalies were generally indicative of heatwaves. Approximately 42% (39%) of heatwave days during daytime (nighttime) coincided with LST anomalies; conversely, 51% of daytime LST anomalies overlapped with the exact days of a heatwave (38% at night). Importantly, the degree of association was significantly higher for extremely hot days (up to an 80% match) and long-lasting heatwaves (up to an 85% match). Rising trends in frequency and duration were observed for both heatwaves and LST anomalies. The results advance the understanding of surface-atmosphere coupling during extreme temperature days and reflect the suitability of thermal remote sensing in heatwave preparedness strategies.

This paper discusses the main achievements of DISARM (Drought and fIre ObServatory and eArly waRning system) project, which developed an early warning system for wildfires in the Eastern Mediterranean. The four pillars of this system include (i) forecasting wildfire danger, (ii) detecting wildfires with remote sensing techniques, (iii) forecasting wildfire spread with a coupled weather-fire modeling system, and (iv) assessing the wildfire risk in the frame of climate change. Special emphasis is given to the innovative and replicable parts of the system. It is shown that for the effective use of fire weather forecasting in different geographical areas and in order to account for the local climate conditions, a proper adjustment of the wildfire danger classification is necessary. Additionally, the consideration of vegetation dryness may provide better estimates of wildfire danger. Our study also highlights some deficiencies of both EUMETSAT (Exploitation of Meteorological Satellites) and LSA-SAF (Satellite Application Facility on Land Surface Analysis) algorithms in their skill to detect wildfires over islands and near the coastline. To tackle this issue, a relevant modification is proposed. Furthermore, it is shown that IRIS, the coupled atmosphere-fire modeling system developed in the frame of DISARM, has proven to be a valuable supporting tool in fire suppression actions. Finally, assessment of the wildfire danger in the future climate provides the necessary context for the development of regional adaptation strategies to climate change.

Storm cells that evolve in Mesoscale Convective Systems (MCSs) can be recognised with the use of satellite images. In this study, Meteosat images are used for the early detection and monitoring of the evolution of storm cells associated with MCSs. The developed methodology is based on the estimation of the "Airmass" and "Convective storm" composites, at fifteen minutes intervals. The methodology was applied on a selected four-day case study in February 2013, when a depression was developed over Africa and moved across the Mediterranean resulting in deep convection along its trajectory and in an extreme weather event (heavy rainfall associated with severe flooding) at the wider urban agglomeration of Athens. The produced composites detect potential vorticity (PV) anomaly related to cyclogenesis and increase the potential to detect and monitor storm cells associated with natural hazards.

Current research has shown that extreme events can be interrelated and can trigger cascading impacts. Traditional climate risk and impact assessments typically consider a single extreme event, a fact that leads to the underestimation of risks, as such events are often interdependent. This work will address the field of extreme climate and weather through the assessment of compound drought and heatwave (CDHW) events. The principal aim of this study is to evaluate the current state of climate over the Mediterranean in terms of droughts and heatwaves, focusing on their compound effects, and identifying their large-scale atmospheric circulation drivers. The identification of the CDHW climatology is carried out through the improvement of a novel index tailored to the Mediterranean region. Multiple combinations are examined according to the generic definition of CDHW events (i.e., heatwaves that occur during the period of drought events) and an optimum compound index is developed that successfully quantifies the interrelations of droughts and heatwaves. Extensive statistical analysis is carried out to evaluate their frequency, duration, intensity, and trends. The analysis is based on the high-resolution state-of-the-art ERA5-Land reanalysis product at a 0.1° x 0.1° horizontal resolution. Following a data-driven methodology, the role of large-scale atmospheric circulation drivers on the onset, duration, and intensity of the CDHW events is examined using the synoptic climatology approach. The ‘environment-to-circulation’ approach is selected that employs the Self-Organizing Map (SOM) artificial neural networks for clustering and mapping the atmospheric circulation for enabling the understanding of the large-scale drivers responsible for CDHW events. The analysis provides the hot-spot regions of CDHW high-impact phenomena over the Mediterranean region and their association with large-scale circulation is established.