Hourly Time Scale Research Articles

The Unique Behavior of Vertical Velocity in Developing Deep Convection

AbstractWhen daytime tropical convection develops away from mesoscale disturbances, it typically transitions gradually from dry to shallow to deep convection on hourly timescales. The transition is commonly associated with the formation of larger horizontal boundary‐layer structures and an increasing level of cloud organization aloft. This study demonstrates that a spectral analysis of the resolved high‐resolution sub‐cloud flow features allows for a robust identification of dominant length scales and the quantification of their growth rates during the transition. Furthermore, it is shown that temperature, moisture, and horizontal winds develop multiple length scales with the largest ones growing up to several kilometers in magnitude. However, the vertical velocity behaves in a distinct manner developing significantly smaller values, comparable over land and ocean. This indicates stronger inherent limits of vertical velocity to self‐organize by size.

Incorporating Diurnal and Meter-Scale Variations of Ambient CO2 Concentrations in Development of Direct Air Capture Technologies.

To be implemented on climate-relevant scales, direct air capture of CO2 (DAC) will require large capital-intensive facilities and careful attention to cost minimization. In making decisions among potential sites for DAC facilities, all of the factors that will impact process cost and efficiency should be considered. In this paper we focus on a factor that has previously received little attention in the DAC community, namely variations in atmospheric conditions on hourly time scales and length scales of meters. We present data curated from extensive previous studies of biosphere-atmosphere fluxes with observations of CO2 concentration, temperature, and relative humidity (RH) with hourly resolution from many sites in North America. These include locations where typical diurnal variations in CO2 concentration during summer months exceeds 150 ppm. These variations are larger than the seasonal variations that exist between averaged CO2 concentrations in winter and summer, and they are highly correlated with diurnal variations in temperature and RH. Diurnal variations are dependent on the height above ground at which CO2 concentrations are measured, with smaller variations existing at heights of 10 m or more than at ground level. We illustrate the potential implications of these short-term variations for the operation and optimization of a DAC process with process-level calculations for a specific adsorption-based process using amine-rich adsorbents.

Emergence hour-by-hour of r-process features in the kilonova AT2017gfo

The spectral features in the optical/near-infrared counterparts of neutron star mergers (kilonovae, KNe) evolve dramatically on hourly timescales. To examine the spectral evolution, we compiled a temporal series that was complete at all observed epochs from 0.5 to 9.4 days of the best optical/near-infrared (NIR) spectra of the gravitational-wave detected kilonova AT2017gfo. Using our analysis of this spectral series, we show that the emergence times of spectral features place strong constraints on line identifications and ejecta properties, while their subsequent evolution probes the structure of the ejecta. We find that the most prominent spectral feature, the 1 μm P Cygni line, appears suddenly, with the earliest detection at 1.17 days. We find evidence in this earliest feature for the fastest yet discovered kilonova ejecta component at 0.40–0.45c. Across the observed epochs and wavelengths, the velocities of the line-forming regions span nearly an order of magnitude, down to as low as 0.04–0.07c. The time of emergence closely follows the predictions for Sr II because Sr III combines rapidly under local thermal equilibrium (LTE) conditions. The transition time between the doubly and singly ionised states provides the first direct measurement of the ionisation temperature. This temperature is highly consistent with the temperature of the emitted blackbody radiation field at a level of a few percent. Furthermore, we find the KN to be isotropic in temperature, that is, the polar and equatorial ejecta differ by less than a few hundred Kelvin or ≲5%, in the first few days post-merger based on measurements of the reverberation time-delay effect. This suggests that a model with very simple assumptions, with single-temperature LTE conditions, reproduces the early kilonova properties surprisingly well.

Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory-based approach.

In this study, six individual machine learning (ML) models and a stacked ensemble model (SEM) were used for daytime visibility estimation at Bangkok airport during the dry season (November-April) for 2017-2022. The individual ML models are random forest, adaptive boosting, gradient boosting, extreme gradient boosting, light gradient boosting machine, and cat boosting. The SEM was developed by the combination of outputs from the individual models. Furthermore, the impact of factors affecting visibility was examined using the Shapley Additive exPlanation (SHAP) method, an interpretable ML technique inspired by the game theory-based approach. The predictor variables include different air pollutants, meteorological variables, and time-related variables. The light gradient boosting machine model is identified as the most effective individual ML model. On an hourly time scale, it showed the best performance across three out of four metrics with the ρ = 0.86, MB = 0, ME = 0.48km (second lowest), and RMSE = 0.8km. On a daily time scale, the model performed the best for all evaluation metrics with ρ = 0.92, MB = 0.0km, ME = 0.3km, and RMSE = 0.43km. The SEM outperformed all the individual models across three out of four metrics on an hourly time scale with ρ = 0.88, MB = 0.0km, (second lowest), and RMSE = 0.75km. On the daily scale, it performed the best with ρ = 0.93, MB = 0.02km, ME = 0.27km, and RMSE = 0.4km. The seasonal average original (VISorig) and meteorologically normalized visibility (VISnorm) decrease from 2017 to 2021 but increase in 2022. The rate of decrease in VISorig is double than rate of decrease in VISnorm which suggests the effect of meteorology visibility degradation. The SHAP analysis identified relative humidity (RH), PM2.5, PM10, day of the season year (i.e., Julian day) (JD), and O3 as the most important variables affecting visibility. At low RH, visibility is not sensitive to changes in RH. However, beyond a threshold, a negative correlation between RH and visibility is found potentially due to the hygroscopic growth of aerosols. The dependence of the Shapley values of PM2.5 and PM10 on RH and the change in average visibilities under different RH intervals also suggest the effect of hygroscopic growth of aerosol on visibility. A negative relationship has been identified between visibility and both PM2.5 and PM10. Visibility is positively correlated with O3 at lower to moderate concentrations, with diminishing impact at very high concentrations. The JD is strongly negatively related to visibility during winter while weakly associated positively later in summer. Findings from this research suggest the feasibility of employing machine learning techniques for predicting visibility and understanding the factors influencing its fluctuations.

The wind-solar hybrid energy could serve as a stable power source at multiple time scale in China mainland

The instability of wind and solar power hinders their penetration into electrical transmission networks. Hybrid wind-solar power generation can mitigate the instability of wind or solar power. However, research on complementary methods and the temporal distribution of wind and solar energies remains insufficient. In this study, well-validated and used high-resolution reanalysis data were used to explore the complementarity between wind and solar power on multiple time scales across China mainland. Researchers have found that wind and solar energies are strongly complementary from seasonal to hourly time scales. Wind-solar hybrid power generation can increase the availability of renewable energy by 15%–25 %, and a continuous renewable power supply can be achieved during daytime hours. In addition, the authors found that the complementary strength between wind and solar power could be enhanced by adjusting their proportions. This study highlights that hybrid wind-solar systems can provide a stable energy source. The complementary deployment of wind and solar energies should be considered in future applications.

Optimal hybrid power plants for electric vehicle charging demand

Transmission constraints, increasing motivations to decarbonize, and concerns over peak electric vehicle (EV) load impacts on local grids have driven electric customers to consider behind-the-meter, hybrid power plant generation and storage at the distributed-grid level for EV charging. In this study, we develop capabilities to optimize hybrid power plant component capacities for EV charging. We then demonstrate these capabilities in a case study for Boulder, Colorado, using public EV charging data as well as wind and solar resource data.Our results show system designs that balance the cost of energy with load-meeting and peak shaving performance. Within the case study, systems designed for wind, solar photovoltaic (PV), and storage resulted in lower cost of energy than those optimized for PV and storage only. This indicates that in areas where wind resource exists, hybrid power plants that include wind, PV, and battery assets can better meet EV charging loads (including peak loads that are prone to overloading local grids) than PV and battery assets alone. Future work to address limitations in this paper include extending cost modeling to include performance losses (e.g., based on operations or weather) and charging station costs to estimate levelized cost of charging, and quantifying uncertainty and error in our aggregation methods for estimating EV charging loads at the hourly timescale.

Does a convection-permitting regional climate model bring new perspectives on the projection of Mediterranean floods?

Abstract. Floods are the primary natural hazard in the French Mediterranean area, causing damages and fatalities every year. These floods are triggered by heavy precipitation events (HPEs) characterized by limited temporal and spatial extents. A new generation of regional climate models at the kilometer scale have been developed, allowing an explicit representation of deep convection and improved simulations of local-scale phenomena such as HPEs. Convection-permitting regional climate models (CPMs) have been scarcely used in hydrological impact studies, and future projections of Mediterranean floods remain uncertain with regional climate models (RCMs). In this paper, we use the CNRM-AROME CPM (2.5 km) and its driving CNRM-ALADIN RCM (12 km) at the hourly timescale to simulate floods over the Gardon d'Anduze catchment located in the French Mediterranean region. Climate simulations are bias-corrected with the CDF-t method. Two hydrological models, a lumped and conceptual model (GR5H) and a process-based distributed model (CREST), forced with historical and future climate simulations from the CPM and from the RCM, have been used. The CPM model confirms its ability to better reproduce extreme hourly rainfall compared to the RCM. This added value is propagated on flood simulation with a better reproduction of flood peaks. Future projections are consistent between the hydrological models but differ between the two climate models. Using the CNRM-ALADIN RCM, the magnitude of all floods is projected to increase. With the CNRM-AROME CPM, a threshold effect is found: the magnitude of the largest floods is expected to intensify, while the magnitude of the less severe floods is expected to decrease. In addition, different flood event characteristics indicate that floods are expected to become flashier in a warmer climate, with shorter lag time between rainfall and runoff peak and a smaller contribution of base flow, regardless of the model. This study is a first step for impact studies driven by CPMs over the Mediterranean.

The dynamics of nocturnal sap flow components of a typical revegetation shrub species on the semiarid Loess Plateau, China.

The components of nighttime sap flux (En), which include transpiration (Qn) and stem water recharge (Rn), play important roles in water balance and drought adaptation in plant communities in water-limited regions. However, the quantitative and controlling factors of En components are unclear. This study used the heat balance method to measure sap flow density in Vitex negundo on the Loess Plateau for a normal precipitation year (2021) and a wetter year (2022). The results showed that the mean values were 1.04 and 2.34g h-1 cm-2 for Qn, 0.19 and 0.45g h-1 cm-2 for Rn in 2021 and 2022, respectively, and both variables were greater in the wetter year. The mean contributions of Qn to En were 79.76% and 83.91% in 2021 and 2022, respectively, indicating that the En was mostly used for Qn. Although the vapor pressure deficit (VPD), air temperature (Ta) and soil water content (SWC) were significantly correlated with Qn and Rn on an hourly time scale, they explained a small fraction of the variance in Qn on a daily time scale. The main driving factor was SWC between 40-200cm on a monthly time scale for the Qn and Rn variations. Rn was little affected by meteorological and SWC factors on a daily scale. During the diurnal course, Qn and Rn initially both declined after sundown because of decreasing VPD and Ta, and Qn was significantly greater than Rn, whereas the two variables increased when VPD was nearly zero and Ta decreased, and Rn was greater than Qn. These results provided a new understanding of ecophysiological responses and adaptation of V. negundo plantations to increasing drought severity and duration under climate changes.

Improving Satellite Chlorophyll-a Retrieval in the Turbid Waters of the Bay of Fundy, Canada

AbstractThe Bay of Fundy is a highly productive ecosystem within the Northwest Atlantic where extreme tides and strong currents result in a large gradient of sediment concentrations across and along the bay. We processed daily satellite data from the MODerate resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite from 2003 to 2021 at 300-m resolution to understand and quantify spatial and temporal trends in chlorophyll-a concentration (chl-a, a measure of phytoplankton biomass), and suspended particulate matter concentration (SPM) in the Bay of Fundy surface waters. To account for high sediment loading (up to 100’s g m−3) and moderate chl-a (median in situ chl-a of 1.5 mg m−3 from 2003 to 2021), coefficients of the OC3M chl-a algorithm were regionally tuned using in situ chl-a data, and satellite-derived SPM was incorporated within the chl-a retrieval algorithm to account for possible bias. The updated new algorithm was denoted as OCX-SPMCor. Chl-a computed using OCX-SPMCor showed better performance against in situ chl-a than the generic OC3M with a coefficient of determination that increased from 0.01 to 0.28 and a root mean square logarithmic error that decreased by 35%. Unlike previous remote sensing studies, OCX-SPMCor correctly predicted the particular chl-a seasonality in the Bay of Fundy, which does not follow the typical occurrence of spring/fall blooms as observed in the adjacent Gulf of Maine and Scotian Shelf. For the first time, satellite-predicted chl-a aligned with the phenology of in situ chl-a, where chl-a continually increased from April to June and remained high all summer, with a small secondary summer peak before decreasing in the fall. SPM seasonality followed an opposite trend where SPM reached a maximum in winter and a minimum in summer. A small number of matchups and high temporal variability on the hourly time scale precluded a robust assessment of the satellite-derived SPM. However, comparisons between time series of remotely sensed and in situ SPM demonstrated the ability of the satellite-derived SPM to capture temporal variations, though the absolute values may be slightly underestimated. Accurate maps of phytoplankton biomass and sediment concentrations are essential variables required for effective management and conservation of marine ecosystems in the Bay of Fundy.

High-resolution projections of outdoor thermal stress in the twenty-first century: a Tasmanian case study

To adapt to Earth’s rapidly changing climate, detailed modelling of thermal stress is needed. Dangerous stress levels are becoming more frequent, longer, and more severe. While traditional measurements of thermal stress have focused on air temperature and humidity, modern measures including radiation and wind speed are becoming widespread. However, projecting such indices has presented a challenging problem, due to the need for appropriate bias correction of multiple variables that vary on hourly timescales. In this paper, we aim to provide a detailed understanding of changing thermal stress patterns incorporating modern measurements, bias correction techniques, and hourly projections to assess the impact of climate change on thermal stress at human scales. To achieve these aims, we conduct a case study of projected thermal stress in central Hobart, Australia for 2040–2059, compared to the historical period 1990–2005. We present the first hourly metre-scale projections of thermal stress driven by multivariate bias-corrected data. We bias correct four variables from six dynamically downscaled General Circulation Models. These outputs drive the Solar and LongWave Environmental Irradiance Geometry model at metre scale, calculating mean radiant temperature and the Universal Thermal Climate Index. We demonstrate that multivariate bias correction can correct means on multiple time scales while accurately preserving mean seasonal trends. Changes in mean air temperature and UTCI by hour of the day and month of the year reveal diurnal and annual patterns in both temporal trends and model agreement. We present plots of future median stress values in the context of historical percentiles, revealing trends and patterns not evident in mean data. Our modelling illustrates a future Hobart that experiences higher and more consistent numbers of hours of heat stress arriving earlier in the year and extending further throughout the day.

Enhancing Solar Radiation Forecasting in Diverse Moroccan Climate Zones: A Comparative Study of Machine Learning Models with Sugeno Integral Aggregation

Hourly solar radiation (SR) forecasting is a vital stage in the efficient deployment of solar energy management systems. Single and hybrid machine learning (ML) models have been predominantly applied for precise hourly SR predictions based on the pattern recognition of historical heterogeneous weather data. However, the integration of ML models has not been fully investigated in terms of overcoming irregularities in weather data that may degrade the forecasting accuracy. This study investigated a strategy that highlights interactions that may exist between aggregated prediction values. In the first investigation stage, a comparative analysis was conducted utilizing three different ML models including support vector machine (SVM) regression, long short-term memory (LSTM), and multilayer artificial neural networks (MLANN) to provide insights into their relative strengths and weaknesses for SR forecasting. The comparison showed the proposed LSTM model had the greatest contribution to the overall prediction of six different SR profiles from numerous sites in Morocco. To validate the stability of the proposed LSTM, Taylor diagrams, violin plots, and Kruskal–Wallis (KW) tests were also utilized to determine the robustness of the model’s performance. Secondly, the analysis found coupling the models outputs with aggregation techniques can significantly improve the forecasting accuracy. Accordingly, a novel aggerated model that integrates the forecasting outputs of LSTM, SVM, MLANN with Sugeno λ-measure and Sugeno integral named (SLSM) was proposed. The proposed SLSM provides spatially and temporary interactions of information that are characterized by uncertainty, emphasizing the importance of the aggregation function in mitigating irregularities associated with SR data and achieving an hourly time scale forecasting accuracy with improvement of 11.7 W/m2.

Evaluation of GSMaP Version 8 Precipitation Products on an Hourly Timescale over Mainland China

A thorough evaluation of the recently released Global Satellite Mapping of Precipitation (GSMaP) is critical for both end-users and algorithm developers. In this study, six products from three versions of GSMaP version 8, including real time (NOW-R and NOW-C), near real time (NRT-R and NRT-C), and post-real time (MVK-R and MVK-C), are systematically and quantitatively evaluated based on time-by-time observations from 2167 stations in mainland China. Among each version, both products with and without gauge correction are adopted to detect the gauge correction effect. Error quantification is carried out on an hourly timescale. Three common statistical indices (i.e., correlation coefficient (CC), relative bias (RB), and root mean square error (RMSE)) and three event detection capability indices (i.e., probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI)) were adopted to analyze the inversion errors in precipitation amount and precipitation event frequency across the various products. Additionally, in this study, we examine the dependence of GSMaP errors on rainfall intensity and elevation. The following main results can be concluded: (1) MVK-C exhibits the best ability to retrieve rainfall on the hourly timescale, with higher CC values (0.31 in XJ to 0.47 in SC), smaller RMSE values (0.14 mm/h in XJ to 0.99 mm/h in SC), and lower RB values (−4.78% in XJ to 16.03% in NC). (2) Among these three versions, the gauge correction procedure plays a crucial role in reducing errors, especially in the post-real-time version. After being corrected, MVK-C demonstrates an obvious CC value improvement (>0.3 on the hourly timescale) in various sub-regions, increasing the percentage of sites with CC values above 0.5 from 0.03% (MVK-R) to 28.47% (MVK-C). (3) GSMaP products generally exhibit error dependencies on precipitation intensity and elevation, particularly in areas with drastic elevation changes (such as 1200–1500 m and 3000–3300 m), where the accuracy of satellite precipitation estimates is significantly affected. (4) CC values decreased with an increasing rainfall intensity; RB and RMSE values increased with an increasing rainfall intensity. The results of this study may be helpful for algorithm developers and end-users and provide a scientific reference for different hydrological applications and disaster risk reduction.

Detectability of Fast Radio Burst Optical Counterparts with the Future Chinese Wide Field Telescopes

Fast Radio Bursts (FRBs) are extra-galactic origin milli-second duration bright radio bursts. Theoretically, FRBs may produce optical counterparts with durations from milliseconds to hours. The FRB optical counterparts may be detectable in future large field telescopes, including the China Space Station Telescope (CSST), the 2.5-meter Wide Field Survey Telescope (WFST) lead by the University of Science and Technology of China (USTC) and the Purple Mountain Observatory (PMO), and the Earth 2.0 (ET). The fast radio burst optical counterparts are grouped into millisecond time-scale optical counterparts, hourly time-scale optical counterparts, and optical afterglow for our study. The first two can be generated by the high-energy extension of the radio radiation of fast radio bursts and the inverse Compton scattering of high-energy electrons. The event rates highly depend on the optical-to-radio flux ratio ην. For millisecond duration optical counterparts, the detection rate of WFST, CSST, and ET can reach hundreds per year in an ideal case. If ην∼10−3, the corresponding annual detection rates of WFST and CSST are in the order of 1, and the annual detection rate of ET is 19.5. For the hourly timescale optical counterparts, ideally, the age of the supernova remnant is 5 years, ην is about 10−6, and the annual detection rates are above 100. The X-ray counterpart of FRB 200428 indicates that FRBs may produce relativistic outflow, which will interact with the interstellar medium to produce optical afterglows. Combined with the standard afterglow model, the detectability of optical afterglow is explored with a simulation of fast radio bursts following the redshift and energy distribution from the literature. With a total energy-radio energy ratio similar to FRB 200428, (ζ=105), the estimated annual detection rates of CSST, WFST, and ET are 1.3, 1.0, and 67, respectively.

Exploiting district cooling network and urban building energy modeling for large-scale integrated energy conservation analyses

Research effort in analyzing the energy consumption of buildings in districts or cities is increasing as new paradigms of distributed energy production and sharing are spreading. In this work, the Urban Building Energy Model of the Quad, an area of the University of California Davis campus, is presented, validated, and analyzed for possible actions to reduce the district cooling energy consumption. Energy conservation measures involve buildings' air handling units and district chilled water generation. To investigate this system, a district cooling networks module has been developed in EUReCA, the Urban Building Energy Modeling tool used for the analyses. The model has been validated with energy demand and temperature data from 2018 to 2020, resulting in a district cooling demand deviation lower than 7% for 2018 and 2019. Normalized Root Mean Square Error is lower than 35% for each building, proving the reliability at the hourly time scale. Systems energy reduction actions like heat recovery units' installation and heuristic control of the air supply cooling temperature are beneficial in reducing the cooling demand, and they allow a more efficient discharging of the chilled water storage, reducing the average electricity price by load shifting during peak demand.

Water isotopic characterisation of the cloud–circulation coupling in the North Atlantic trades – Part 1: A process-oriented evaluation of COSMOiso simulations with EUREC4A observations

Abstract. Naturally available, stable, and heavy water molecules such as HDO and H218O have a lower saturation vapour pressure than the most abundant light water molecule H216O; therefore, these heavy water molecules preferentially condense and rain out during cloud formation. Stable water isotope observations thus have the potential to provide information on cloud processes in the trade-wind region, in particular when combined with high-resolution model simulations. In order to evaluate this potential, nested COSMOiso (isotope-enabled Consortium for Small Scale Modelling; Steppeler et al., 2003; Pfahl et al., 2012) simulations with explicit convection and horizontal grid spacings of 10, 5, and 1 km were carried out in this study over the tropical Atlantic for the time period of the EUREC4A (Elucidating the role of clouds-circulation coupling in climate; Stevens et al., 2021) field experiment. The comparison to airborne in situ and remote sensing observations shows that the three simulations are able to distinguish between different mesoscale cloud organisation patterns as well as between periods with comparatively high and low rain rates. Cloud fraction and liquid water content show a better agreement with aircraft observations with higher spatial resolution, because they show strong spatial variations on the scale of a few kilometres. A low-level cold-dry bias, including too depleted vapour in the subcloud and cloud layer and too enriched vapour in the free troposphere, is found in all three simulations. Furthermore, the simulated secondary isotope variable d-excess in vapour is overestimated compared to observations. Special attention is given to the cloud base level, which is the formation altitude of shallow cumulus clouds. The temporal variability of the simulated isotope variables at cloud base agrees reasonably well with observations, with correlations of the flight-to-flight data as high as 0.7 for δ2H and d-excess. A close examination of isotopic characteristics under precipitating clouds, non-precipitating clouds, clear-sky and dry-warm patches at the altitude of cloud base shows that these different environments are represented faithfully in the model with similar frequencies of occurrence, isotope signals, and specific-humidity anomalies as found in the observations. Furthermore, it is shown that the δ2H of cloud base vapour at the hourly timescale is mainly controlled by mesoscale transport and not by local microphysical processes, while the d-excess is mainly controlled by large-scale drivers. Overall, this evaluation of COSMOiso, including the isotopic characterisation of different cloud base environments, suggests that the simulations can be used for investigating the role of atmospheric circulations on different scales for controlling the formation of shallow cumulus clouds in the trade-wind region, as will be done in part 2 of this study.

Meteorological Analysis on Extremely Depleted 18O Rainfall Events during the Summer in Adelaide, Australia

Abstract The transport of atmospheric water vapor plays a crucial role in the production of precipitation and the variation of precipitation isotopic composition (δ18Op). This study investigates three precipitation events with extremely depleted heavy isotopes in the summer of Adelaide, Australia. Using fundamental water vapor diagnosis and moisture calculation method, this research analyzes the impact of rainout degrees along moisture transport paths, atmospheric circulation patterns, water vapor sources, and moisture transport on the extreme depletion of precipitation isotopes in the study area. The purpose of this study is to reveal the direct cause of extremely depleted δ18Op at the hourly time scale. The results show the diversity and complexity of δ18Op variation in summer precipitation events in Adelaide. The rainout caused by local and upstream large precipitation may be the main reason for extremely low values of δ18Op. The phenomenon of subcloud secondary evaporation, which is driven by the interaction between relatively low humidity and high temperature at near-surface levels, plays a pivotal role in the entire precipitation process. This mechanism is particularly pronounced during the onset or cessation of precipitation events, thereby resulting in the observed enrichment of δ18Op values. The oxygen stable isotopic composition of water vapor (δ18Oa) would usually become higher, when the air mass mixes with new moisture with relatively high δ18Oa, suppressing the influence of the previous rainout.

Representing decentralized generation and local energy use flexibility in an energy system optimization model

Local energy generation and energy use flexibility is becoming increasingly relevant for planning energy systems with a high level of renewables, electrification, and decentralization. Although current energy system models represent various flexibility options, capturing demand-side flexibility and prosumption remains challenging. In light of these challenges, this study presents a national-scale energy system optimization model covering the residential and commercial sectors of Belgium and considering the interplay between local energy use flexibility from electric vehicle (EV) charging, battery storage, rooftop photovoltaic (PV) systems and distribution grids. To adequately represent energy use flexibility, the model adopts a high temporal resolution, consisting of three representative weeks at an hourly time scale. The role of prosumption is analysed by introducing a prosumer potential reflecting the extent to which local energy production can be used to meet local demand ‘behind the meter’. The results show that flexibility significantly reduces total system costs with reduced distribution grid capacity investment by 48% by 2050. Furthermore, local energy use flexibility enables a significant uptake of rooftop PV (+6.7 GW), but only under a high prosumer potential condition. Finally, a curtailment of around 5–7% of PV production is cost-effective to sustain a more continuous use of the distribution grid at low peak capacity. To increase the robustness of the results, future research should consider expanding the model's sector coverage, adding additional flexibility sources, improving the representation of prosumption, soft-linking with distribution grid models and a better representation of mobility patterns.

Automatic Detection of Rainfall at Hourly Time Scales from Mooring Near-Surface Salinity in the Eastern Tropical Pacific

Abstract We explore the use of ocean near-surface salinity (NSS), that is, salinity at 1-m depth, as a rainfall occurrence detector for hourly precipitation using data from the Salinity Processes in the Upper-Ocean Regional Studies–2 (SPURS-2) mooring at 10°N, 125°W. Our proposed unsupervised learning algorithm consists of two stages. First, an empirical quantile-based identification of dips in NSS enables us to capture most events with hourly averaged rainfall rate of >5 mm h−1. Overestimation of precipitation duration is then corrected locally by fitting a parametric model based on the salinity balance equation. We propose a local precipitation model composed of a small number of calibration parameters representing individual rainfall events and their location in time. We show that unsupervised rainfall detection can be formulated as a statistical problem of predicting these variables from NSS data. We present our results and provide a validation technique based on data collected at the SPURS-2 mooring. Significance Statement Continuous monitoring of precipitation in the ocean is challenging when a physical rain gauge is not available in the region of interest. Indirect detection of precipitation using available data, such as changes in ocean near-surface salinity (NSS) can be used to construct a virtual rainfall detector. We propose to combine data-based and model-based methods to detect rainfall without the use of a physical rain gauge. We use NSS and precipitation data from a mooring in the eastern tropical Pacific Ocean to develop and test the method.

Estimation of Real-Time Rainfall Fields Reflecting the Mountain Effect of Rainfall Explained by the WRF Rainfall Fields

The effect of mountainous regions with high elevation on hourly timescale rainfall presents great difficulties in flood forecasting and warning in mountainous areas. In this study, the hourly rainfall–elevation relationship of the regional scale is investigated using the hourly rainfall fields of three storm events simulated by Weather Research and Forecasting (WRF) model. From this relationship, a parameterized model that can estimate the spatial rainfall field in real time using the hourly rainfall observation data of the ground observation network is proposed. The parameters of the proposed model are estimated using eight representative pixel pairs in valleys and mountains. The proposed model was applied to the Namgang Dam watershed, a representative mountainous region in the Korea, and it was found that as elevation increased in eight selected pixel pairs, rainfall intensity also increased. The increase in rainfall due to the mountain effect was clearly observed with more rainfall in high mountainous areas, and the rainfall distribution was more realistically represented using an algorithm that tracked elevation along the terrain. The proposed model was validated using leave-one-out cross-validation with seven rainfall observation sites in mountainous areas, and it demonstrated clear advantages in estimating a spatial rainfall field that reflects the mountain effect. These results are expected to be helpful for flood forecasting and warning, which need to be calculated quickly, in mountainous areas. Considering the importance of orographic effects on rainfall spatial distribution in mountainous areas, more storm events and physical analysis of environmental factors (wind direction, thermal cycles, and mountain slope angle) should be continuously studied.

A Synthesis Method to Generate Hourly Electricity Production Time-series of Wind Plants in Peru for Long-term Expansion Planning

Plenty of works have treated the system expansion planning problem in the presence of intermittent renewable energy resources like wind. However, most of those proposals have been approached from scenarios of plenty of data, which is not the rule in developing countries, where principal investment actors have recently switched their focus. In contrast to operation problems where existing literature can be successfully applied since it requires short-term historical time-series gathered from the same studied plants, proposals for planning problems are almost impossible to apply because of a lack of information and measurement about renewable resources in places where no renewable plants have been previously installed. In order to fill this information gap, this paper presents a novel methodology to synthesize wind production time-series on an hourly time scale, taking as inputs aggregate data such as monthly wind speed average values and Weibull annual parameters. The methodology comprises four steps, from data gathering to calculating electrical power produced by a wind farm. Three application tests are performed for different places in India, Chile, and Peru to validate the proposed methodology. The results show that the methodology successfully synthesizes time-series of output power, correctly achieves persistence characteristics, and slightly over or underestimates the produced wind energy, having a discrepancy of 6.2% in the yearly total.