Simone Lolli

A recently proposed two-stage spatial enhancement methodology for the thermal infrared (TIR) observations of Landsat 9 is integrated into a new framework for the generation of high-spatial-resolution Land Surface Temperature (LST). The methodology formulates the enhancement of the TIRS-2 observations as a multimodal data fusion problem, exploiting the statistical similarity between the TIR data and the visible and near-infrared (VNIR) and shortwave infrared (SWIR) bands available from the Operational Land Imager (OLI-2) at 30 m spatial resolution, together with its 15 m panchromatic (PAN) band. In the first stage, the TIR observations are hyper-sharpened from their native 100 m resolution to 30 m through dedicated linear combinations of the VNIR and SWIR bands. In the second stage, the 30 m TIR top-of-atmosphere (TOA) radiance is further spatially enhanced using the 15 m PAN band, yielding 15 m TIR TOA radiance. In the new framework, the enhanced TOA radiance is subsequently converted into LST through a Level-1-based single-channel retrieval. Starting from the original Landsat 9 Band-10 TOA radiance, the proposed reconstruction exploits the atmospheric and surface ancillary parameters provided with the USGS Level-2 product to estimate the surface-leaving radiance and retrieve LST through Planck-law inversion. The retrieval is first validated at the native 100 m TIRS spatial resolution against the USGS Collection-2 Level-2 Band-10 surface-temperature product. The validated radiance-to-LST transformation is then applied to the 30 m and 15 m spatially enhanced Band-10 TOA radiance, yielding an LST product at 15 m spatial resolution. The framework is evaluated on a summer Landsat 9 acquisition over Shanghai, China, characterized by strong thermal and spatial heterogeneity. The results demonstrate the feasibility of combining radiance-level spatial enhancement with a physically consistent single-channel retrieval to generate high-resolution LST, providing a basis for future applications to the analysis of urban thermal patterns and urban heat islands.

Observational estimates of aerosol–cloud interaction are persistently confounded by meteorological co-variability. We use four years (18 June 2021–31 August 2025) of continuous NASA Micropulse Lidar Network (MPLNET) profiling at the EPA-NCU site in northern Taiwan (24.97◦N, 121.18◦E) to quantify the statistical association between sub-cloud aerosol loading and cloud base height (CBH), conditioned on five objectively classified Synoptic Weather Patterns (SWPs) and on boundary-layer depth. From 183 536 quality-controlled aerosol–cloud co-occurrence profiles we find negative log–log slopes in every synoptic regime, from m = −0.224 ± 0.007 under northeast monsoon continental outflow to m = −0.381 ± 0.017 under weakly forced local thermal circulations. Slopes steepen under shallow boundary layers by factors of 1.2 to 2.7. A multivariate regression including reanalysis sub-cloud relative humidity, boundary-layer depth and synoptic regime retains a negative aerosol partial slope, but the variance uniquely attributable to aerosol is only ∆R² = 0.76%. We show that the aerosol proxy and the cloud search window share the mixing-layer top as a common reference level, which produces a negative slope on geometric grounds alone, and we therefore interpret these results as statistical co-variability rather than as a measured aerosol effect. The magnitude of the regime dependence implies that all-season, single-regime lidar diagnostics are not representative over monsoon East Asia.

We present an unsupervised procedure for near-real-time mapping of advected aerosols, namely desert dust and biomass-burning smoke, at urban scale. A differential, surface-normalized spectral index (DBB-2) is computed from four spectrally overlapping bands of Sentinel-2 MSI (B2, B3, B4, B5; 10 m) and Sentinel-3 OLCI (Oa04, Oa06, Oa08, Oa11; 300 m). The 10m Sentinel-2 map, available every five days in cloud-free conditions, is fused at feature level with the near-daily 300m OLCI map, so that aerosol loading is updated in each 300m cell at least every two days while the sub-cell spatial texture is extrapolated from the latest Sentinel-2 acquisition. The sign of DBB-2 separates scattering-dominated dust (positive) from absorption-dominated smoke (negative), and its modulus tracks the aerosol optical depth (AOD). The clear-sky reference is selected from Sentinel-2 products alone, so no ground-based auxiliary data are needed. Validation against AERONET spectral deconvolution (SDA) AOD at Potenza (Italy, Saharan dust, March–April 2024) and Barcelona (Spain, smoke, October 2020) yields agreement of about 96% and 95%, respectively. A full account of the method is given in Ref. 1.

Satellite remote sensing of methane (CH₄) offers the potential to monitor emissions across large scales, but skepticism remains among regulators, policymakers, and the scientific community of both the precision and accuracy of such estimations due to unquantified observational uncertainties. Using 500 perturbation experiments with four error distributions applied to TROPOMI XCH₄ over East, South, and Central Asia, we systematically assess where and when divergence‑based emission estimates are reliable. We show that the shape of the uncertainty distribution – not just its magnitude – governs error propagation and thus effects the stability of derived fluxes. Furthermore we show that uncertainty propogates over both space and time, indicating that purely local accounting may lead to an underestimate of uncertainty. Coal‑dominated industrial grids retain trustworthy signals (30% retention of valid perturbations), whereas agricultural and mixed‑use grids retain only 16%, indicating that current filtering practices are insufficient for these land types. These results provide a practical, uncertainty‑aware filter for satellite‑derived methane emissions, directly supporting the Global Methane Pledge and national greenhouse gas inventories by identifying where satellite data can be trusted (coal‑dominated regions) and where additional caution or alternative approaches are needed. The process is data-neutral and can be extended to other satellite platforms and adopted by other regions around the globe.

Satellite remote sensing of methane (CH4) offers the potential to monitor emissions across large scales, but skepticism remains among regulators, policymakers, and the scientific community of both the precision and accuracy of such estimations due to unquantified observational uncertainties. Using 500 perturbation experiments with four error distributions applied to TROPOMI XCH4 over East, South, and Central Asia, we systematically assess where and when divergence‑based emission estimates are reliable. We show that the shape of the uncertainty distribution – not just its magnitude – governs error propagation and thus effects the stability of derived fluxes. Furthermore we show that uncertainty propogates over both space and time, indicating that purely local accounting may lead to an underestimate of uncertainty. Coal‑dominated industrial grids retain trustworthy signals (30% retention of valid perturbations), whereas agricultural and mixed‑use grids retain only 16%, indicating that current filtering practices are insufficient for these land types. These results provide a practical, uncertainty‑aware filter for satellite‑derived methane emissions, directly supporting the Global Methane Pledge and national greenhouse gas inventories by identifying where satellite data can be trusted (coal‑dominated regions) and where additional caution or alternative approaches are needed. The process is data-neutral and can be extended to other satellite platforms and adopted by other regions around the globe.

The interaction between urban heat island (UHI) and heatwaves (HWs) shapes urban extreme heat, but whether HW-induced UHI amplification reorganises canopy-to-boundary-layer thermal structure, and whether local climate zones (LCZs) modulate this response, remains unclear. Here, we investigated two July 2020 HW events in the Guangzhou metropolitan area in southern China, using 1 km LCZ-coupled Weather Research and Forecasting (WRF) simulations. We analysed the vertical thermal response of the urban area and individual LCZs using canopy and boundary-layer UHI intensities (CUHII, BUHII), layer-mean urban–rural air-temperature differences (Layer ∆T ), and surface-energy-balance and turbulence indicators. HWs amplified both CUHII and BUHII in a vertically non-uniform manner: CUHII increased more than BUHII, and additional warming concentrated in the canopy and lower boundary layer. Warming propagated upward as the daytime boundary layer developed but contracted toward the surface at night and during transition periods, with upper-level responses weakening or reversing. LCZ heterogeneity was largely confined to the lowest ∼250 m: LCZ 1 showed the strongest additional warming, LCZ 2 and LCZ 4 reached comparable levels through different energy–turbulence pathways, and LCZ 6 was weakest. Stronger sensible heat flux, higher Bowen ratios, sustained nocturnal heat sources, and reduced low-level stability jointly drove this diurnal reorganisation. During HW1, residual-layer warm anomalies in LCZ 1 ( 0.41 °C at 500–600 m) persisted through the night and provided an additional heat reservoir for next-day mixed-layer development, whereas LCZ 6 showed much weaker memory effects. HW2 reproduced the inter-LCZ ranking with weaker absolute amplitude.

This study investigates the applicability of the Morphological Image Processing Approach (MIPA) for estimating the Atmospheric Boundary Layer Height (ABLH) from ceilometer observations. Based on our previous work, in which MIPA was assessed and validated using High Power LiDAR observations, the present study extends its application for the first time to three commercial ceilometers (CHM15k, CL31, and CL51). The analysis is based on a measurement campaign conducted at the CIAO (CNR-IMIOT Atmospheric Observatory) during spring 2024. Three case studies covering different atmospheric conditions were selected to quantify the retrieval performance. A grid search was performed to optimize MIPA for each ceilometer, while the retrieved ABLH values were evaluated against a reference dataset obtained from temporally frequent radiosonde launches. The retrievals were also compared with the proprietary algorithms implemented by the instrument manufacturers. Results demonstrate that MIPA provides robust ABLH estimates for the considered ceilometers and generally outperforms the corresponding operational retrievals. The optimization showed that only a limited subset of processing parameters required instrument-specific tuning, confirming the robustness of the proposed methodology. Among the investigated instruments, the CHM15k achieved the best overall performance, yielding the lowest RMSE and standard error with respect to radiosonde-derived ABLH. These results highlight the influence of sensor characteristics, such as signal-to-noise ratio and near-range measurement quality, on retrieval accuracy. Widespread availability, low cost, and continuous operation, together with MIPA's low computational cost, make ceilometers an attractive complement to advanced LiDAR systems, enabling denser and more continuous near-real-time ABLH monitoring networks at regional and potentially continental scales.

The height of the planetary boundary layer directly influences local and regional climatic phenomena, making its study and estimation of vital importance for environmental sciences. The main objective of this work was to create a gridded map of planetary boundary layer height across the European continent, with a spatial resolution of 25 km and monthly mean values at two synoptic hours (12:00 and 00:00 UTC). We implemented the regression kriging method by combining various data sources, including observations, climatic and topographic variables, and reanalysis data (ERA5), and different regression methods (linear, random forest, and gradient boosting) for the 2010–2020 period. In both UTC hours, combining reanalysis, land surface temperature and topographic covariates with random forest regression provided the best performance. However, the models' performance at night declined considerably compared with midday. Then, we compared our seasonal predictions with reanalysis data and found a consistently higher spatio-temporal accuracy than that of the ERA5 reanalysis. For example, at 12:00 UTC, spatial variability in winter showed RMSE values ≤170 m, compared with ≥250 m for ERA5, while temporal variability in summer reached RMSE values ≤260 m, versus 340 m for ERA5. At 00:00 UTC, spatial variability in summer achieved RMSE values = 14 m, whereas ERA5 exhibited RMSE = 60 m, while temporal variability in winter reached RMSE values ≤176 m, versus 200 m for ERA5. The proposed regression kriging method thus performed well in estimating the height of the boundary layer at 12:00 UTC, and to a lesser extent at 00:00 UTC.

The interaction between urban heat island (UHI) and heatwaves (HWs) shapes urban extreme heat, but whether HW-induced UHI amplification reorganises canopy-to-boundary-layer thermal structure, and whether local climate zones (LCZs) modulate this response, remains unclear. Here, we investigated two July 2020 HW events in the Guangzhou metropolitan area in southern China, using 1 km LCZ-coupled Weather Research and Forecasting (WRF) simulations. We analysed the vertical thermal response of the urban area and individual LCZs using canopy and boundary-layer UHI intensities (CUHII, BUHII), layer-mean urban–rural air-temperature differences (Layer ∆T ), and surface-energy-balance and turbulence indicators. HWs amplified both CUHII and BUHII in a vertically non-uniform manner: CUHII increased more than BUHII, and additional warming concentrated in the canopy and lower boundary layer. Warming propagated upward as the daytime boundary layer developed but contracted toward the surface at night and during transition periods, with upper-level responses weakening or reversing. LCZ heterogeneity was largely confined to the lowest ∼250 m: LCZ 1 showed the strongest additional warming, LCZ 2 and LCZ 4 reached comparable levels through different energy–turbulence pathways, and LCZ 6 was weakest. Stronger sensible heat flux, higher Bowen ratios, sustained nocturnal heat sources, and reduced low-level stability jointly drove this diurnal reorganisation. During HW1, residual-layer warm anomalies in LCZ 1 ( 0.41 °C at 500–600 m) persisted through the night and provided an additional heat reservoir for next-day mixed-layer development, whereas LCZ 6 showed much weaker memory effects. HW2 reproduced the inter-LCZ ranking with weaker absolute amplitude.