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The avocado cv. Hass is one of the most dynamic fruits in the world and is of particular significance in tropical areas, where climate variability phenomena have a high impact on productivity and sustainability. Nanotechnology-based tools could be an alternative to mitigate and/or adapt plants to these phenomena. Our approach was based on identifying changes in temperature and precipitation associated with climate variability in avocado areas in Colombia and proposing mitigation strategies based on the use of nanotechnology. This study had two objectives: (i) to identify variations in temperature and precipitation in avocado-producing areas in Colombia and (ii) to evaluate the effect of calcium phosphate nanoparticles (nano CP) as an alternative to reduce stress in avocados under simulate climatic variability condition. Climatic clusters were determined based on the spatial K-means method and with the climatic temporal series data (1981–2020), a time series analysis we carried out. Later changes in each cluster were simulated in growth chambers, evaluating physiological and developmental responses in avocado seedlings subjected to nanoCaP after adjusting the application form and dose. XRD diffraction shows that the calcium phosphate phases obtained by solution combustion correspond to a mixture of hydroxyapatite and witocklite nanoparticles with irregular morphologies and particle sizes of 100 nm. Three clusters explained ∼90% of the climate variation, with increases and decreases in temperature and precipitation in the range of 1-1.4 °C and 4.1–7.3% respectively. The best-fitted time series models were of stationary autoregressive integrated moving averages (SARIMA). The avocado seedlings had differential responses (P

Hurricanes are a dominant disaster in the Caribbean, always causing serious power outages throughout the islands. Hurricane Maria was a prime example, causing unimaginable destruction of the power infrastructure of Puerto Rico (PR). Consequently, one month after the hurricane landfall, approximately 80% of the population was still without power. After an event of such massive destruction, the electric power restoration process progresses very slowly. This timeline can be improved using power outage (PO) forecast models that help identify the vulnerable places before the hurricane landfall. Generally, these models are trained with historical power outages records, associated data on weather conditions, and additional information about the natural and built environments. However, PO records are often difficult to acquire, and, in many instances, the power utility companies may not record them. This study utilizes a satellite-based Visible Infrared Imaging Radiometer Suite (VIIRS) night light data product as a surrogate for the power delivery to predict hurricane-induced PO in areas having limited to nonexistent historical data records. The processed satellite data is then used along with geographic variables, and simulated weather data to formulate machine learning-based algorithms to predict PO for future hurricane events. These models are applied and validated in the context of the PR catastrophic storm, Hurricane Maria.

One of the greatest challenges to mankind is understanding the underlying principles of climate change. Over the last years, the role of forests in climate change has received increased attention. This is due to the observation that not only the atmosphere has a principal impact on vegetation growth but also that vegetation is contributing to local variations of weather resulting in diverse microclimates. The interconnection of plant ecosystems and weather is described and studied as ecoclimates. In this work we take steps towards simulating ecoclimates by modeling the feedback loops between vegetation, soil, and atmosphere. In contrast to existing methods that only describe the climate at a global scale, our model aims at simulating local variations of climate. Specifically, we model tree growth interactively in response to gradients of water, temperature and light. As a result, we are able to capture a range of ecoclimate phenomena that have not been modeled before, including geomorphic controls, forest edge effects, the Foehn effect and spatial vegetation patterning. To validate the plausibility of our method we conduct a comparative analysis to studies from ecology and climatology. Consequently, our method advances the state-of-the-art of generating highly realistic outdoor landscapes of vegetation. [ABSTRACT FROM AUTHOR]

In the estimation of future investments in the offshore wind industry, the operation and maintenance (O&M) phase plays an important role. In the simulation of the O&M figures, the weather conditions should contain information about the waves' main characteristics and the wind speed. As these parameters are correlated, they were simulated by using a multivariate approach, and thus by generating vectors of measurements. Four different stochastic weather time series generators were investigated: Markov chains (MC) of first and second order, vector autoregressive (VAR) models, and long short-term memory (LSTM) neural networks. The models were trained on a 40-year data set with 1 h resolution. Thereafter, the models simulated 25-year time series, which were analysed based on several time series metrics and criteria. The MC (especially the one of second order) and the VAR model were shown to be the ones capturing the characteristics of the original time series the best. The novelty of this paper lies in the application of LSTM models and multivariate higher-order MCs to generate offshore weather time series, and to compare their simulations to the ones of VAR models. Final recommendations for improving these models are provided as conclusion of this paper. [ABSTRACT FROM AUTHOR]

A new methodology to calculate and visualize the amount of airborne sea salt with a 100m mesh resolution in high definition has been proposed in this study. A transport calculation module for airborne sea salt is implemented to the atmospheric component of the multi-scale atmosphereocean coupled model. The computational fluid dynamics model (NuWiCC-ST) results that can calculate the generation and transport of sea salt particles are incorporated to the transport calculation. The coupled methodology has been applied to create a corrosion environment map for Choshi City. The reliability of the developed method was confirmed by comparing airborne sea salt data measured using the dry gauze method with the calculated data. [ABSTRACT FROM AUTHOR]

Due to the complex interplay of various meteorological phenomena, simulating weather is a challenging and open research problem. In this contribution, we propose a novel physics-based model that enables simulating weather at interactive rates. By considering atmosphere and pedosphere we can define the hydrologic cycle - and consequently weather - in unprecedented detail. Specifically, our model captures different warm and cold clouds, such as mammatus, hole-punch, multi-layer, and cumulonimbus clouds as well as their dynamic transitions. We also model different precipitation types, such as rain, snow, and graupel by introducing a comprehensive microphysics scheme. The Wegener-Bergeron-Findeisen process is incorporated into our Kessler-type microphysics formulation covering ice crystal growth occurring in mixed-phase clouds. Moreover, we model the water run-off from the ground surface, the infiltration into the soil, and its subsequent evaporation back to the atmosphere. We account for daily temperature changes, as well as heat transfer between pedosphere and atmosphere leading to a complex feedback loop. Our framework enables us to interactively explore various complex weather phenomena. Our results are assessed visually and validated by simulating weatherscapes for various setups covering different precipitation events and environments, by showcasing the hydrologic cycle, and by reproducing common effects such as Foehn winds. We also provide quantitative evaluations creating high-precipitation cumulonimbus clouds by prescribing atmospheric conditions based on infrared satellite observations. With our model we can generate dynamic 3D scenes of weatherscapes with high visual fidelity and even nowcast real weather conditions as simulations by streaming weather data into our framework. [ABSTRACT FROM AUTHOR]

Global warming is expected to greatly impact the thermal conditions of inland water bodies. Publicly available weather forecasts sufficiently resolve weather patterns above large lakes to satisfactorily force a lake hydrodynamics model, we suspect this is not the case for smaller lakes. We address this issue using weather outputs from a numerical weather prediction model at three different resolutions (3 km, 1 and 0.33 km) to force a hydrodynamics model of Lake George, NY for the month of June 2017. We find that increasing the resolution of the weather model improves the accuracy of the simulation, in part through a significant increase in total heat content of the lake. This warming is linked to an increase in downward heat transfer from the atmosphere in the higher resolution experiment, especially in the northern part of the lake where the lake is less than 2 km wide and the simulated surface winds are stronger. Interrogation of the weather model shows the stronger winds originate from a better representation of the land‐lake interface (and thereby surface roughness length) at higher resolution. At the end of the one month simulation, we find the hypolimnetic volume of Lake George has reduced by 15% in the 0.33 km experiment compared to the 3 km experiment. If this discrepancy were systemic it would have obvious consequences for predicting the ecoysystem function over an extended period of small‐to‐medium sized lakes. Key Points: Higher resolution weather forecast lead to an increase of accuracy in surface water temperature and thermocline depthImprovements in simulated water temperature are linked to a better representation of land use index in the weather modelA mismatch of resolution in the weather forecast leads to an overestimation of hypolimnetic volume by 15% [ABSTRACT FROM AUTHOR]

The complex interplay of a number of physical and meteorological phenomena makes simulating clouds a challenging and open research problem. We explore a physically accurate model for simulating clouds and the dynamics of their transitions. We propose first-principle formulations for computing buoyancy and air pressure that allow us to simulate the variations of atmospheric density and varying temperature gradients. Our simulation allows us to model various cloud types, such as cumulus, stratus, and stratoscumulus, and their realistic formations caused by changes in the atmosphere. Moreover, we are able to simulate large-scale cloud super cells - clusters of cumulonimbus formations - that are commonly present during thunderstorms. To enable the efficient exploration of these stormscapes, we propose a lightweight set of high-level parameters that allow us to intuitively explore cloud formations and dynamics. Our method allows us to simulate cloud formations of up to about 20 km × 20 km extents at interactive rates. We explore the capabilities of physically accurate and yet interactive cloud simulations by showing numerous examples and by coupling our model with atmosphere measurements of real-time weather services to simulate cloud formations in the now. Finally, we quantitatively assess our model with cloud fraction profiles, a common measure for comparing cloud types. [ABSTRACT FROM AUTHOR]

Featured Application: High-resolution weather analysis and forecasts for warnings and civil protection against landslides and hydrological instability. Mountain landscapes are characterised by a very variable environment under different points of view (topography, geology, meteorological conditions), and they are frequently affected by mass wasting processes. A debris flow that occurred along the Croso stream, located in the Italian Lepontine Alps in the Northern Ossola Valley, during summer 2019, was analysed from a geological/geomorphological and meteorological point of view. The debris flow was triggered by an intense precipitation event that heavily impacted a very restricted area over the course of three hours. A previous debris flow along the same stream occurred in Autumn 2000, but it was related to an intense and prolonged rainfall event. The slope was characterised in terms of sediment connectivity, and data were retrieved and elaborated from the Web-GIS (Web-Geographic Information System) database of the IFFI-Italian Landslide Inventory and historical archives of landslides. Both the events were analysed through the weather research and forecasting (WRF) model applying a very high horizontal grid spacing with the aim of catching the precipitation patterns and timings. The obtained results are compared with the observed precipitation at a selection of weather stations in the area. The simulation of WRF that measured the timing in total precipitation and in its minor steps could be considered reliable. Moreover, it reveals to be appropriate for detecting in advance the meteorological conditions potentially triggering mass-wasting processes affecting slopes featuring high connectivity conditions and lithotypes characterised by a high Landslide Susceptibility Index. [ABSTRACT FROM AUTHOR]

Mountain landscapes are characterised by a very variable environment under different points of view (topography, geology, meteorological conditions), and they are frequently affected by mass wasting processes. A debris flow that occurred along the Croso stream, located in the Italian Lepontine Alps in the Northern Ossola Valley, during summer 2019, was analysed from a geological/geomorphological and meteorological point of view. The debris flow was triggered by an intense precipitation event that heavily impacted a very restricted area over the course of three hours. A previous debris flow along the same stream occurred in Autumn 2000, but it was related to an intense and prolonged rainfall event. The slope was characterised in terms of sediment connectivity, and data were retrieved and elaborated from the Web-GIS (Web-Geographic Information System) database of the IFFI-Italian Landslide Inventory and historical archives of landslides. Both the events were analysed through the weather research and forecasting (WRF) model applying a very high horizontal grid spacing with the aim of catching the precipitation patterns and timings. The obtained results are compared with the observed precipitation at a selection of weather stations in the area. The simulation of WRF that measured the timing in total precipitation and in its minor steps could be considered reliable. Moreover, it reveals to be appropriate for detecting in advance the meteorological conditions potentially triggering mass-wasting processes affecting slopes featuring high connectivity conditions and lithotypes characterised by a high Landslide Susceptibility Index.

The avocado cv. Hass is one of the most dynamic fruits in the world and is of particular significance in tropical areas, where climate variability phenomena have a high impact on productivity and sustainability. Nanotechnology-based tools could be an alternative to mitigate and/or adapt plants to these phenomena. Our approach was based on identifying changes in temperature and precipitation associated with climate variability in avocado areas in Colombia and proposing mitigation strategies based on the use of nanotechnology. This study had two objectives: (i) to identify variations in temperature and precipitation in avocado-producing areas in Colombia and (ii) to evaluate the effect of calcium phosphate nanoparticles (nano CP) as an alternative to reduce stress in avocados under simulate climatic variability condition. Climatic clusters were determined based on the spatial K-means method and with the climatic temporal series data (1981-2020), a time series analysis we carried out. Later changes in each cluster were simulated in growth chambers, evaluating physiological and developmental responses in avocado seedlings subjected to nanoCaP after adjusting the application form and dose. XRD diffraction shows that the calcium phosphate phases obtained by solution combustion correspond to a mixture of hydroxyapatite and witocklite nanoparticles with irregular morphologies and particle sizes of 100 nm. Three clusters explained ∼90% of the climate variation, with increases and decreases in temperature and precipitation in the range of 1-1.4 °C and 4.1-7.3% respectively. The best-fitted time series models were of stationary autoregressive integrated moving averages (SARIMA). The avocado seedlings had differential responses ( P<0.05 ) depending on the clusters, with a decrease in physiological behavior and development between 10 and 35%. Additionally, the nanoCaP reduced the climatic stress ( P< 0.05 ) in a range between 10 and 22.5%. This study identified the negative effect of climate variability on avocado seedlings and how nanoCaP can mitigate these phenomena., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

We designed simulations for the high-temperature event that occurred on 23 July 2003 in East China using a series of forecast lead times, from short-range to medium-range, and four land surface schemes (LSSs) (i.e., SLAB, NOAH, RUC, and PX) in the Weather Research and Forecasting Model (WRF), Version 3. The sensitivities of short and medium-range simulations to the LSSs systematically varied with the lead times. In general, the model reproduced short-range, high-temperature distributions. The simulated weather was sensitive to the LSSs, and the LSS-induced sensitivity was higher in the medium range than in the short-range. Furthermore, the LSS performances were complex, i.e., the PX errors apparently increased in the medium range (longer than 6 days), RUC produced the maximum errors, and SLAB and NOAH had approximately equivalent errors that slightly increased. Additional sensitivity simulations revealed that the WRF modeling system assigns relatively low initial soil moisture for RUC and that soil moisture initialization plays an important role that is comparable to the LSS choice in the simulations. LSS-induced negative feedback between surface air temperature (SAT) and atmospheric circulation in the lower atmosphere was found in the medium range. These sensitivities were mainly caused by the LSS-induced differences in surface sensible heat flux and by errors associated with the lead times. Using the SAT equation, further diagnostic analyses revealed LSS deficiencies in simulating surface fluxes and physical processes that modify the SAT and indicated the main reasons for these deficiencies. These results have implications for model improvement and application. [ABSTRACT FROM AUTHOR]

We introduce a stochastic weather generator for the variables of minimum temperature, maximum temperature and precipitation occurrence. Temperature variables are modeled in vector autoregressive framework, conditional on precipitation occurrence. Precipitation occurrence arises via a probit model, and both temperature and occurrence are spatially correlated using spatial Gaussian processes. Additionally, local climate is included by spatially varying model coefficients, allowing spatially evolving relationships between variables. The method is illustrated on a network of stations in the Pampas region of Argentina where nonstationary relationships and historical spatial correlation challenge existing approaches. [ABSTRACT FROM AUTHOR]

Accurate and timely prediction of weather phenomena, such as hurricanes and flash floods, require high-fidelity compute intensive simulations of multiple finer regions of interest within a coarse simulation domain. Current weather applications execute these nested simulations sequentially using all the available processors, which is sub-optimal due to their sub-linear scalability. In this work, we present a strategy for parallel execution of multiple nested domain simulations based on partitioning the 2-D processor grid into disjoint rectangular regions associated with each domain. We propose a novel combination of performance prediction, processor allocation methods and topology-aware mapping of the regions on torus interconnects. Experiments on IBM Blue Gene systems using WRF show that the proposed strategies result in performance improvement of up to 33% with topology-oblivious mapping and up to additional 7% with topology-aware mapping over the default sequential strategy. [ABSTRACT FROM AUTHOR]

Abstract: The eddy-resolving Japan Sea (East Sea) data assimilation affects the mid-level troposphere via vertical wind in the early stage of an explosively developing extratropical cyclone on 5–8 November 2006, and its influence further propagates toward the subpolar and polar regions. Two types of atmospheric responses (convective and gravity-wave patterns) to a sea surface temperature (SST) difference resulting from the ocean data assimilation are found in the early stage of the cyclone development over the western and central Japan Sea. A gravity-wave (convective) pattern appears when the near-surface atmosphere is stable (unstable). The atmospheric signals induced by the SST anomaly resulting from assimilation of the ocean circulation model are subject to advection and can deform with time owing to nonlinearity and instability. The differences in atmospheric and surface temperatures are widely and rapidly spread over the polar region by strong synoptic-scale cyclonic advection. On the other hand, the SST effects on vertical flow and precipitation are limited to narrow areas around the northern Japan Sea and the Pacific cold front in the fully developed stage. Such a developing cyclone is potentially important as a transporter of the meteorological influence over the Japan Sea toward the polar region. [Copyright &y& Elsevier]

The article presents a study that verifies the k-nearest neighbours (k-NN) approach for the prediction of daily weather consequences. The method can be implemented on the assumption that the weather during the target year is similar to the weather recorded in the past. It uses the nearest-neighbour re-sampling method for the simultaneous forecast of daily radiation, maximum and minimum temperature, and precipitation for multiple locations. The study concluded that the k-NN approach on the basis of pattern recognition can be considered as a reliable method to forecast daily weather sequences according historical weather data.

The article examines the growth of maize and pasture in dryland agriculture in southern Africa in an effort to demonstrate spatial modeling of risk in natural resource management. It investigated a method of incorporating spatial coherence into MarkSim, an application designed to generate synthetic daily weather files using Markov model parameters, and demonstrated how it increases the variance of production. Using the findings of a Global Circulation Model, the authors found that low maize yields will likely be experienced in marginal areas.

Making decisions in natural resource management involves an understanding of the risk and uncertainty of the outcomes, such as crop failure or cattle starvation, and of the normal spread of the expected production. Hedging against poor outcomes often means lack of investment and slow adoption of new methods. At the household level, production instability can have serious effects on income and food security. At the national level, it can have social and economic impacts that may affect all sectors of society. Crop models such as CERES-Maize are excellent tools for assessing weather-related production variability. WATBAL is a water balance model that can provide robust estimates of the potential growing days for a pasture. These models require large quantities of daily weather data that are rarely available. MarkSim is an application for generating synthetic daily weather files by estimating the third-order Markov model parameters from interpolated climate surfaces. The models can then be run for each distinct point on the map. This paper examines the growth of maize and pasture in dryland agriculture in southern Africa. Weather simulators produce independent estimates for each point on the map; however, we know that a spatial coherence of weather exists. We investigated a method of incorporating spatial coherence into MarkSim and show that it increases the variance of production. This means that all of the farmers in a coherent area share poor yields, with important consequences for food security, markets, transport, and shared grazing lands. The long-term aspects of risk are associated with global climate change. We used the results of a Global Circulation Model to extrapolate to the year 2055. We found that low maize yields would become more likely in the marginal areas, whereas they may actually increase in some areas. The same trend was found with pasture growth. We outline areas where further work is required before these tools and methods can address natural resource management problems in a comprehensive manner at local community and policy levels.