Climatology and trends of downward shortwave radiation over Brazil

Zuluaga, Cristian Felipe; Ávila-Díaz, Alvaro; Justino, Flávio; Wilson, Aaron B.

doi:10.1016/j.atmosres.2020.105347

Cited by 22 publications

(8 citation statements)

References 71 publications

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“…However, important uncertainties remain in tropical regions due to the lack of data available to analyze (Hersbach et al 2020 ). In some studies, ERA5 has demonstrated reliable estimates of climate features in some regions of Central and South America (Avila-Diaz et al 2020b ; Balmaceda‐Huarte et al 2021 ; Cerón et al 2021 ; Zuluaga et al 2021 ).…”

Section: Methodsmentioning

confidence: 99%

Current and Future Climate Extremes Over Latin America and Caribbean: Assessing Earth System Models from High Resolution Model Intercomparison Project (HighResMIP)

et al. 2022

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Extreme temperature and precipitation events are the primary triggers of hazards, such as heat waves, droughts, floods, and landslides, with localized impacts. In this sense, the finer grids of Earth System models (ESMs) could play an essential role in better estimating extreme climate events. The performance of High Resolution Model Intercomparison Project (HighResMIP) models is evaluated using the Expert Team on Climate Change Detection and Indices (ETCCDI) over the 1981–2014 period and future changes (2021–2050) under Shared Socioeconomic Pathway SSP5–8.5, over ten regions in Latin America and the Caribbean. The impact of increasing the horizontal resolution in estimating extreme climate variability on a regional scale is first compared against reference gridded datasets, including reanalysis, satellite, and merging products. We used three different groups based on the resolution of the model’s grid (sg): (i) low (0.8° ≤ sg ≤ 1.87°), (ii) intermediate (0.5° ≤ sg ≤ 0.7°), and (iii) high (0.23° ≥ sg ≤ 0.35°). Our analysis indicates that there was no clear evidence to support the posit that increasing horizontal resolution improves model performance. The ECMWF-IFS family of models appears to be a plausible choice to represent climate extremes, followed by the ensemble mean of HighResMIP in their intermediate resolution. For future climate, the projections indicate a consensus of temperature and precipitation climate extremes increase across most of the ten regions. Despite the uncertainties presented in this study, climate models have been and will continue to be an important tool for assessing risk in the face of extreme events.

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Section: Methodsmentioning

confidence: 99%

Current and Future Climate Extremes Over Latin America and Caribbean: Assessing Earth System Models from High Resolution Model Intercomparison Project (HighResMIP)

et al. 2022

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“…This error correction strategy is a common way to improve the accuracy of PET estimation (Gebremedhin et al., 2022). Potential data biases in the meteorological input variables may also contribute to the error term in PET calculations (Weedon et al., 2014; Zuluaga et al., 2021), and such biases at a specific grid cell can be corrected based on meteorological conditions and land surface attributes of neighboring cells (L. Han et al., 2021; Xia et al., 2023). In this study, a 3D convolutional (Conv3D) NN was adopted in the error correction to account for the influence of meteorological and land surface conditions from neighboring cells on a specific grid cell.…”

Section: Methodsmentioning

confidence: 99%

Distributed Hydrological Modeling With Physics‐Encoded Deep Learning: A General Framework and Its Application in the Amazon

Wang,

Jiang,

Zheng

et al. 2024

Water Resources Research

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While deep learning (DL) models exhibit superior simulation accuracy over traditional distributed hydrological models (DHMs), their main limitations lie in opacity and the absence of underlying physical mechanisms. The pursuit of synergies between DL and DHMs is an engaging research domain, yet a definitive roadmap remains elusive. In this study, a novel framework that seamlessly integrates a process‐based hydrological model encoded as a neural network (NN), an additional NN for mapping spatially distributed and physically meaningful parameters from watershed attributes, and NN‐based replacement models representing inadequately understood processes is developed. Multi‐source observations are used as training data, and the framework is fully differentiable, enabling fast parameter tuning by backpropagation. A hybrid DL model of the Amazon Basin (∼6 × 106 km2) was established based on the framework, and HydroPy, a global‐scale DHM, was encoded as its physical backbone. Trained simultaneously with streamflow observations and Gravity Recovery and Climate Experiment satellite data, the hybrid model yielded median Nash‐Sutcliffe efficiencies of 0.83 and 0.77 for dynamic and distributed simulations of streamflow and total water storage, respectively, 41% and 35% higher than those of the original HydroPy model. Replacing the original Penman‒Monteith formulation in HydroPy with a replacement NN produces more plausible potential evapotranspiration (PET) estimates, and unravels the spatial pattern of PET in this giant basin. The NN used for parameterization was interpreted to identify the factors controlling the spatial variability in key parameters. Overall, this study lays out a feasible technical roadmap for distributed hydrological modeling in the big data era.

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“…A massive portion of the country lies within the intertropical zone, characterized by high solar radiation values. Averages often exceed 200 watts per square meter over the vast expanses of the territory (Zuluaga et al, 2021). Given this, the deployment of climate-control technologies in facilities dedicated to dairy production is crucial to counteract the detrimental effects of thermal stress on livestock (Becker et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Climate control in the feeding area for Girolando cows: Effects on environmental and production variables

Sousa,

Passini,

Almeida

et al. 2023

Rev. bras. eng. agríc. ambient.

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High-production cows require facilities providing comfortable environments to enhance production efficiency and sustain milk quality. This study evaluated the impact of climatizing the feeding area post-morning and afternoon milking on environmental variables, thermal comfort indices, and the yield and quality of milk in lactating Girolando cows. The experiment followed a 4 × 4 Latin square design with four treatments across four experimental periods, using four cows per treatment. The post-milking climate control treatments included: shading; shading + ventilation; shading + ventilation + shower; and an evaporative adiabatic cooling system. The evaporative adiabatic cooling system proved most effective in maintaining thermal conditions near the comfort level for dairy cows, as indicated by lower thermal comfort indices. This system also enhanced morning and overall milk production. However, the climate control systems did not significantly affect the contents of protein, fat, or lactose in the milk.

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Climatology and trends of downward shortwave radiation over Brazil

Cited by 22 publications

References 71 publications

Current and Future Climate Extremes Over Latin America and Caribbean: Assessing Earth System Models from High Resolution Model Intercomparison Project (HighResMIP)

Current and Future Climate Extremes Over Latin America and Caribbean: Assessing Earth System Models from High Resolution Model Intercomparison Project (HighResMIP)

Distributed Hydrological Modeling With Physics‐Encoded Deep Learning: A General Framework and Its Application in the Amazon

Climate control in the feeding area for Girolando cows: Effects on environmental and production variables

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