A5070053262- Meteorological Phenomena and Simulations
- Atmospheric and Environmental Gas Dynamics
- Hydrocarbon exploration and reservoir analysis
- Climate variability and models
- Urban Heat Island Mitigation
- Remote Sensing and Land Use
- Cryospheric studies and observations
- Climate change and permafrost
- Land Use and Ecosystem Services
- Air Quality Monitoring and Forecasting
- Remote-Sensing Image Classification
- Climate Change and Health Impacts
- Plant Water Relations and Carbon Dynamics
- Noise Effects and Management
- Geophysics and Gravity Measurements
- Oceanographic and Atmospheric Processes
- Quality of Life Measurement
- Wind and Air Flow Studies
- Climate Change and Environmental Impact
- Plant Ecology and Soil Science
- Effects of Environmental Stressors on Livestock
University of Lisbon
2019-2024
Instituto Dom Luiz
2023-2024
Land surface temperature (LST) is a key variable in surface-atmosphere energy and water exchanges. The main goals of this study are to (i) evaluate the LST European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim ERA5 reanalyses over Iberian Peninsula using Satellite Application Facility on Surface Analysis (LSA-SAF) product (ii) understand drivers errors reanalysis. Simulations with ECMWF land-surface model offline mode (uncoupled) were carried out compared reanalysis data....
Abstract. Deep learning (DL) methods have recently garnered attention from the climate change community for being an innovative approach to downscaling variables Earth system and global models (ESGCMs) with horizontal resolutions still too coarse represent regional- local-scale phenomena. In context of Coupled Model Intercomparison Project phase 6 (CMIP6), ESGCM simulations were conducted Sixth Assessment Report (AR6) Intergovernmental Panel on Climate Change (IPCC) at ranging 0.70 3.75∘....
Abstract In this study, we show that limitations in the representation of land cover and vegetation seasonality European Centre for Medium‐Range Weather Forecasting (ECMWF) model are partially responsible large biases (up to ∼10°C, either positive or negative depending on region) simulated daily maximum surface temperature (LST) with respect satellite Earth Observations (EOs) products from Land Surface Analysis Satellite Application Facility. The error patterns were coherent offline...
Cities are considered local "hotspots" of climate change, therefore, the improvement urban present description as well future projections is paramount for designing adaptation and mitigation strategies. Physically-based numerical models often have coarse resolutions do not parametrisations to adequately represent physical processes at scale. This article presents an innovative application XGBoost (a machine learning approach) alternative explore improve Madrid. XGBoost's ability reproduce...
Abstract. Earth observations were used to evaluate the representation of land surface temperature (LST) and vegetation coverage over Iberia in two state-of-the-art models (LSMs) – European Centre for Medium-Range Weather Forecasts (ECMWF) Carbon-Hydrology Tiled ECMWF Scheme Surface Exchanges Land (CHTESSEL) Météo-France Interaction between Soil Biosphere Atmosphere model (ISBA) within SURface EXternalisée modeling platform (SURFEX-ISBA) 2004–2015 period. The results showed that daily maximum...
As cities encompass most of the global population, it is crucial to understand effects climate change in an urban context develop tailored adaptation and mitigation strategies. Physically-based numerical models are computationally demanding present scale limitations that complicate representation their on regional-to-local vice-versa. Therefore, often, methodologies (e.g. statistical downscaling) sought complement output for studies. Deep Learning (DL) a growing technology has become...
Urbanization accelerated last century and is expected to continue, with over half of the global population now living in cities, making it crucial assess climate change on urban areas. Urban projections require very high-resolution physically-based numerical models, which are slow computationally expensive, prompting exploration cost-effective alternatives, such as artificial intelligence. Here, we employ Convolutional Neural Networks (CNNs), a type Deep Learning (DL) model, downscale Global...
Cities are considered local “hotspots” of climate change. Urban areas concentrate a large fraction global population, wealth, and emissions, exposing their inhabitants to change impacts. Therefore, the improvement urban present description future projections paramount for designing adaptation mitigation strategies. Global Climate Models state-of-the-art tools projecting climate. However, most simulations have coarse resolutions do not physical parametrisations adequately...
Understanding and modelling the urban climate impacts of change on environment is essential to underpin development adequate adaptation mitigation measures policies. City-scale projections require very high-resolution physically-based models which are commonly time-intensive computationally expensive run. To overcome this problem, cost time effective alternatives, such as Deep Learning, often sought.Here, we present an application two lightweight 3-layer Convolutional Neural Network (CNN)...
Abstract. Cities concentrate people, wealth, emissions, and infrastructure, thus representing a challenge an opportunity for climate change mitigation adaptation. This urgently demands accurate urban projections to help organizations individuals make climate-smart decisions. However, most of the large ensembles global regional model simulations do not include sophisticated parameterizations (e.g., EURO-CORDEX; CMIP5/6). Here, we explore this shortcoming in ERA5 (the latest generation...
High-quality climate information tailored to cities' needs assists decision makers prepare for and adapt change impacts, as well support the transition toward resilient cities. During last decades, two main modelling approaches emerged understand analyse urban generate information. Firstly, meso- microscale models commonly resolve street city scale (1m 1km) through simulating short "weather" type episodes, possibly under conditions. Secondly, regional (RCMs) are currently approaching...
Land surface temperature (LST) is a key variable in surface-atmosphere energy and water exchanges. The main goals of this study are to (i) evaluate the LST European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim ERA5 reanalyses over Iberian Peninsula using Satellite Application Facility on Surface Analysis (LSA-SAF) product (ii) understand drivers errors reanalysis. Simulations with ECMWF land-surface model offline mode (uncoupled) were carried out compared reanalysis data....
Abstract Subseasonal forecasts lie between medium-range and seasonal time scales with an emerging attention due to the relevance in society by scientific challenges involved. This study aims (i) evaluate development of systematic errors lead European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble surface-related variables during late spring summer, (ii) investigate potential relationships predictive skill. The evaluation is performed over northern hemisphere midlatitudes,...
Abstract. Earth observations were used to evaluate the representation of Land Surface Temperature (LST) and vegetation coverage over Iberia in two state-of-the-art land surface models (LSMs) – European Centre for Medium Range Weather Forecasting (ECMWF) Carbon-Hydrology Tiled ECMWF Scheme Exchanges (CHTESSEL) Météo-France Interaction between Soil Biosphere Atmosphere model (ISBA) within SURface EXternalisée modelling platform (SURFEX-ISBA) 2004–2015 period. The results show that daily...
Abstract. Cities concentrate people, wealth, emissions, and infrastructures, thus representing a challenge an opportunity for climate change mitigation adaptation. This places urgent demand accurate urban projections to help organizations individuals making smart-decisions. However, most of the state-of-the-art global regional models have oversimplified representation (or completely neglect) processes. Here, we use city Paris as case study show that this is fifth (and latest) generation...
Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI
Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI
Cities are considered local “hotspots” of climate change. Urban areas concentrate a large fraction global population, wealth, and emissions, exposing their inhabitants to change impacts. Therefore, the improvement urban present description future projections paramount for designing adaptation mitigation strategies. The Global Climate Models state-of-the-art tools projecting climate. However, most simulations have coarse resolutions do not physical parametrisations...
Half of the global population lives in cities and this percentage is expected to increase throughout 21st century. Therefore, we must learn how climate change affect understand properly devise adaptation mitigation policies. Spatial resolutions physically-based models are often either too coarse or expensive run obtain high-resolution high-quality simulations urban environment. Deep Learning a promising computationally efficient technology that has recently been successfully implemented...
Cities are considered local “hotspots” of climate change. Urban areas concentrate a large fraction global population, wealth, and emissions, exposing their inhabitants to change impacts. Therefore, the improvement urban present description future projections paramount for designing adaptation mitigation strategies. Global Climate Models state-of-the-art tools projecting climate. However, most simulations have coarse resolutions do not physical parametrisations adequately...
Abstract. Deep learning (DL) methods have recently garnered attention from the climate change community, as an innovative approach for downscaling variables Earth System and Global Climate Models (ESGCMs) with horizontal resolutions still too coarse to represent regional-to-local-scale phenomena. In context of Coupled Model Intercomparison Project phase 6 (CMIP6), ESGCMs simulations were conducted Sixth Assessment Report (AR6) Intergovernmental Panel on Change (IPCC), at ranging 0.70º 3.75º....
<p>Subseasonal forecasts (ranging between 2 weeks and months) have been the subject of attention in many operational weather centers by research community recent years. This growing stems from value these for society scientific challenges involved. The capturing representing key processes teleconnections which are relevant at scales significant. One example is temperature extremes associated with like heatwaves droughts that can severe consequences nature human health, among...