Production Uncertainty Research Articles

Assessing the Climatological Characteristics of Observed and Simulated Seasonal Onset of Precipitation Over Southern and Eastern Africa

ABSTRACTPrediction of seasonal onset is crucial to agriculture in southern and eastern Africa. Here, we applied two definitions of onset, namely meteorological and agricultural (crop‐germination), to evaluate CMIP6 models through the lens of rainfall onset over representative maize agricultural regions of South Africa, Tanzania, Malawi and Zambia. We use the ERA5 reanalysis as a proxy for observations, and robust regression to calculate a statistical comparison of the onset definitions for the period 1979–2021. Evaluation of ERA5 reanalysis shows similar magnitude and pattern as gauge based MSWEP. Our results show that, for meteorological onset, Johannesburg, with a subtropical highland climate, experienced earliest onset after 23 December; and an increasing trend (later onset) but not statistically significant (p = 0.2). Over Bethlehem, which has continental climate, the earliest onset date was after October 9 and an increasing interannual variability since 2000 is noted. The standard deviation of onset dates across the regions shows an East‐Central‐South gradient. We also found that the crop‐germination onset definition shows earlier onset of seasonal rains, it differs considerably across regions, and has higher interannual variability, in comparison with the meteorological definition. Over Lilongwe, Mbeya and Lusaka, late meteorological onset with a weak positive and insignificant trend is observed. The CMIP6 model's representation of onset trend differs from reanalysis data, with inter‐model differences. Late meteorological onset is underestimated by GFDL‐CM4 and MPI while INM5, MPI and NorESM overestimate the observed earliest onset. The largest bias is shown by INM and MPI which simulate earliest and latest onset as 190 (07 January) and 206 (23 January) respectively. In addition, models often fail to simulate sufficient precipitation to produce onset for seed germination and crop development. The ACCESS model showed an insignificant trend (p value = 2) and later onset over Lilongwe, an insignificant trend (p value = 0.9) over Lusaka, and an earlier onset over Mbeya. Using the agricultural onset definition, over Bethlehem, all the models and the ERA5 reanalysis did not produce enough precipitation to meet onset conditions. We suggest that rainfall onset studies use several definitions or metrics of onset and that the choice of metric be informed by the research question. Using such an ensemble of onset metrics contributes to a better understanding of variability and uncertainties in agricultural productivity.

Hybrid energy system optimization integrated with battery storage in radial distribution networks considering reliability and a robust framework

This research presents a robust optimization of a hybrid photovoltaic-wind-battery (PV/WT/Batt) system in distribution networks to reduce active losses and voltage deviation while also enhancing network customer reliability considering production and network load uncertainties. The best installation position and capacity of the hybrid system (HS) are found via an improved crow search algorithm with an inertia weight technique. The robust optimization issue, taking into account the risk of uncertainty, is described using the gap information decision theory method. The proposed approach is used with 33- and 69-bus networks. The results reveal that the HS optimization in the network reduces active losses and voltage variations, while improving network customer reliability. The robust optimization results show that in the 33-bus network, the system remains resilient to prediction errors under the worst-case uncertainty scenario, with a 44.53% reduction in production and a 22.18% increase in network demand for a 30% uncertainty budget. Similarly, in the 69-bus network, the system withstands a 36.22% reduction in production and a 16.97% increase in load for a 25% uncertainty budget. When comparing stochastic and robust methods, it was found that the stochastic Monte Carlo method could not consistently provide a reliable solution for all objectives under uncertainty, whereas the robust approach successfully managed the maximum uncertainty related to renewable generation and network demand across different uncertainty budgets.

Design of photovoltaic and battery energy storage systems through load demand characterization: A case study in Thailand

The integration of photovoltaic (PV) system at behind the meter has gained popularity due to the growing trend toward environmentally friendly energy solutions. Coupling PV systems with battery energy storage systems (BESS) addresses the uncertainties of PV energy production while enhancing energy management. Load characteristics have influence on PV and BESS design both in technical and economic aspects. This paper presents a comprehensive analysis of load demand characterization methodologies tailored for the design of PV and BESS. The fundamental load properties such as daily maximum power, minimum power, and load energy, categorizing loads based on their daily and annual variation degrees are investigated. The techno-economic performance of PV-only and PV+BESS systems is analyzed, focusing on metrics such as excess energy, peak limiting performance, and economic feasibility using the levelized cost of energy (LCOE). The findings of this study reveal three distinct load characteristics based on daily variation: moderate (50–70 %), moderate to high (70–80 %), and high (80–90 %). Load profiles with moderate variation (50–70 %) demonstrate economic feasibility when sized with a PV-only system matching maximum power demand. With BESS prices reduced to less than 140 USD/kWh, these loads could also integrate a 1C-BESS sized at half capacity under the same control strategy, resulting in lower excess energy and LCOE. Conversely, loads with higher variation (>70 %) and lower self-consumption rates (<70 %) face fewer viable options for both PV-only and PV+BESS configurations, especially if BESS costs remain high. These loads require custom-designed solutions and more extensive load analysis. Through analysis, this study establishes the relationship between load characteristics and system design suitability for the loads studied. The findings underscore the importance of incorporating load demand characterization techniques in the design systems, highlighting the need for tailored approaches based on specific load characteristics and economic viability.

Optimizing Distributed Energy Resources in Microgrid SCUC through Seq2Seq Scheduling Algorithms

In order to optimize Distributed Energy Resources (DERs) inside a microgrid's Security-Constrained Unit Commitment framework, this study investigates the use of Seq2Seq (Sequence-to-Sequence) scheduling methods (SCUC). There is a growing consensus that microgrids are an important part of the future of the electric grid because of the advantages they provide in terms of reliability, renewable energy integration, and overall efficiency. In the context of complicated SCUC issues, efficient scheduling and optimization of DERs are essential for realizing their full potential. Seq2Seq models, a kind of deep learning approach, have shown impressive performance in several applications requiring sequence prediction. Uncertainty in renewable energy production, energy demand forecasts, and security limitations are all addressed in this work as Seq2Seq algorithms are applied to microgrid SCUC. Significant gains in economic efficiency, ecological viability, and compliance with security constraints have been shown. This study lays the way for the development of more effective, sustainable, and resilient energy infrastructure by contributing to the advancement of the area of microgrid optimization.

Uncertainty prediction of conventional gas production in Sichuan Basin under multi factor control

The establishment of a natural gas production model under multi factor control provides support for the formulation of planning schemes and exploration deployment decisions, and is of great significance for the rapid development of natural gas. Especially the growth rate and decline rate of production can be regulated in the planning process to increase natural gas production. The exploration and development of conventional gas in the Sichuan Basin has a long history. Firstly, based on the development of conventional gas production, the influencing factors of production are determined and a production model under multi factor control is established. Then, single factor analysis and sensitivity analysis are conducted, and multi factor analysis is conducted based on Bayesian networks. Finally, combining the multivariate Gaussian mixture model and production sensitivity analysis, a production planning model is established to predict production uncertainty under the influence of multiple factors. The results show that: 1) the production is positively correlated with the five influencing factors, and the degree of influence is in descending order: recovery rate, proven rate, growth rate, decline rate, and recovery degree. After being influenced by multiple factors, the fluctuation range of production increases and the probability of realization decreases. 2) The growth rate controls the amplitude of the growth stage, the exploration rate and recovery rate control the amplitude of the stable production stage, the recovery degree controls the amplitude of the transition from the stable production stage to the decreasing stage, and the decreasing rate controls the amplitude of the decreasing stage. 3)The article innovatively combines multiple research methods to further obtain the probability of achieving production under the influence of multiple factors, providing a reference for the formulation of production planning goals.

Demand side management programs in smart grid through cloud computing

The development of power systems requires smart grids to facilitate real-time control and monitoring using bilateral communications across the power grid. With the implementation of a distributed structure, smart grids will be highly reliable, efficient, and secure, as well as affordable in terms of power management. With the expansion of the power grid at the level of distribution and the smarter equipment used by the shareholders, it is necessary to consider measures to balance production and consumption and to optimize the consumer pattern. One of these approaches is the use of demand-side management programs, in which the system operator seeks to optimize the coordination between production units, storage units, and consumption loads in a way that achieves maximum social welfare. This article attempts to present models for planning a demand-side management program that is presented in the presence of new cloud computing technologies. The model proposed is based on the use of storage units and will be able, in addition to covering uncertainty in renewable production, to customize energy reserves and computational capacity in a way that brings the lowest operating costs.

Optimizing Distributed Energy Resources in Microgrid SCUC through Seq2Seq Scheduling Algorithms

In order to optimize Distributed Energy Resources (DERs) inside a microgrid's Security-Constrained Unit Commitment framework, this study investigates the use of Seq2Seq (Sequence-to-Sequence) scheduling methods (SCUC). There is a growing consensus that microgrids are an important part of the future of the electric grid because of the advantages they provide in terms of reliability, renewable energy integration, and overall efficiency. In the context of complicated SCUC issues, efficient scheduling and optimization of DERs are essential for realizing their full potential. Seq2Seq models, a kind of deep learning approach, have shown impressive performance in several applications requiring sequence prediction. Uncertainty in renewable energy production, energy demand forecasts, and security limitations are all addressed in this work as Seq2Seq algorithms are applied to microgrid SCUC. Significant gains in economic efficiency, ecological viability, and compliance with security constraints have been shown. This study lays the way for the development of more effective, sustainable, and resilient energy infrastructure by contributing to the advancement of the area of microgrid optimization.

Quantifying the value of probabilistic forecasts when trading renewable hybrid power parks in day-ahead markets: A Nordic case study

Renewable hybrid power parks (HPPs) that combine wind power, solar photovoltaic (PV) power and storage have emerged as promising electricity generation resources. However, HPPs face operational challenges due to the uncertainty in power production and electricity prices, which is why probabilistic forecasts that capture the uncertainty associated with forecast errors have gained attention. While the research community has proposed several methods to improve the accuracy of probabilistic forecasts, the question on how these forecasts can improve decision-making over deterministic forecasts is rarely quantified. This study assesses the value of probabilistic forecasts and analyze the improvement compared to deterministic forecasts in day-ahead markets. The value is quantified using almost two years of data from an operational HPP in Sweden. Results show that: (i) high grid connection capacities leverage the value of probabilistic models, (ii) a deterministic model is preferable for parks with a ratio of battery energy capacity to installed nominal power of the renewable power park equal to 0.6 MWh/MW, (iii) a probabilistic model allows utilizing the energy storage more effectively by reducing the energy throughput of the battery with 61%–87%, and (iv) a probabilistic model increases the unit profit when the forecast errors of the regulating price are higher than the spot price, (v) a simple probabilistic benchmark model, which is worse in terms of forecast accuracy, increases the unit profit compared to the analyzed deterministic models, and (vi) the more advanced probabilistic model analyzed in this study does not provide a significant improvement over a simple probabilistic benchmark model.

Machine learning-enabled techno-economic uncertainty analysis of sustainable aviation fuel production pathways

Stochastic techno-economic analysis (TEA) is pivotal in assessing the financial viability and risks inherent in biofuel production processes. In this method, the Monte Carlo approach entails the random sampling of input variables and multiple runs of the TEA model to create probability distributions of economic metrics. However, traditional Monte Carlo TEA, reliant on iterative calls to process simulation, is resource-intensive and time-consuming, hindering widespread adoption. To address these challenges, we present an accessible framework that harnesses machine learning methods to estimate techno-economic uncertainty in biofuel production pathways. Our approach streamlines the conventional simulation process by automating dataset generation and machine learning model training. These trained models enable rapid predictions of minimum fuel selling prices at any scale, accommodating randomized input variables based on their defined distributions. We illustrate the efficacy of our framework through examples from sustainable aviation fuel production pathways. Our research entails identifying the primary factors influencing uncertainties in minimum selling prices, exploring the synergistic effects of pathway inputs, and assessing how price variability is impacted by financial, technical, and supply chain factors. These examples underscore the framework's effectiveness in addressing breakeven price uncertainties in biofuel production across diverse input scenarios.

A robust financing theory of ICOs under demand uncertainty of products of token platforms

PurposeThe market products produced by Initial Coin Offerings (ICO) platforms are often relatively new and have no previous transaction records and therefore are hard to estimate for its demand. The purpose is to study the impacts of the degree of ambiguity aversion of entrepreneurs to demand uncertainty on the ICO financing ratio, the optimal expected output, the optimal efforts and the token price.Design/methodology/approachIn an optimal ICO design, we introduce demand uncertainty of the product and establish a robust optimization method to solve the ICO optimal design. We compare ICO financing and the general venture capital (VC) financing model. We analyze the impact of demand uncertainty on the optimal ICO financing ratio.FindingsFindings include that the ICO financing ratio is positively related to the degree of ambiguity aversion, the token price is negatively related to the degree of ambiguity aversion and the “ambiguity premium” exists in the ICO market, the optimal effort levels are negatively related with the ICO financing ratio, but positively related with token price, and in the environment of high production cost, VC financing is not as good as ICO financing.Originality/valueWe develop a robust ICO financing model by assuming that the entrepreneur is ambiguity aversive to the demand uncertainty. Analyze the impact of the degree of ambiguity aversion on the ICO financing ratio in theory and find that the entrepreneur can raise funds with the higher ICO token ratio when she has a larger degree of ambiguity aversion to the demand uncertainty. Extend the impact analysis of the degree of ambiguity aversion on the expected token price and find a negative relationship between the expected token price and the degree of ambiguity aversion of the entrepreneur to the demand uncertainty.

Impact of intraspecific genetic variation on interspecific competition: a theoretical case study of forage binary mixtures

IntroductionIncreasing intraspecific genetic variation (IV) has been identified as a potential factor to improve productivity and stabilise botanical composition in plant communities. In grasslands systems, this could offer a lever to manage uncertainties of production and variability in the harvested species balance. However, little is known about the conditions to favour IV impact and the mechanisms at play.MethodsThe dependency of IV impact on traits holding it and environmental stressors were analysed using a spatially-explicit individual-based model (IBM) of grassland communities. Sixty-three binary mixtures were defined to reflect a gradient of functional divergence between species regarding light and nitrogen (N) acquisition. The growth and dynamics of these communities were simulated for one year with three possible IV levels under two environments contrasting in terms of soil N fertility.Results and discussionThe model predicted a positive impact of moderate and high IV levels on maintaining the species balance over time, but no marked effects on mixture productivity. This stabilising effect increased at higher IV levels and under low soil N fertility. It also tended to be more pronounced in communities with intermediate functional divergence offering a significant overlap between light and N acquisition parameter values of both species. The major traits involved in the plant response to neighbours differed depending on the most contested resource, as indicated by the within-population selection of individuals with favourable N-related parameters under low N and light-related parameters under high N environments. The hypothesis that IV favours a complementarity of resource use between species was not supported. Rather, a greater spatial heterogeneity in competitive interactions was demonstrated, leading to a higher probability of growth and survival for individuals within the subordinate species. These results highlight the potential usefulness of IV to design forage mixtures with improved stability and resilience.

A generic stochastic network-based formulation for production optimization of district heating systems

AbstractDistrict heating (DH) systems are an important component in the EU strategy to reach the emission goals, since they allow an efficient supply of heat while using the advantages of sector coupling between different energy carriers such as power, heat, gas and biomass. Most DH systems use several different types of units to produce heat for hundreds or thousands of households (e.g. natural gas-fired boilers, electric boilers, biomass-fired units, waste heat from industry, solar thermal units). Furthermore, combined heat and power units units are often included to use the synergy effects of excess heat from electricity production. To address the challenge of providing optimization tools for a vast variety of different system configurations, we propose a generic mixed-integer linear programming formulation for the operational production optimization in DH systems. The model is based on a network structure that can represent different system setups while the underlying model formulation stays the same. Therefore, the model can be used for most DH systems although they might use different combinations of technologies in different system layouts. The mathematical formulation is based on stochastic programming to account for the uncertainty of energy prices and production from non-dispatchable units such as waste heat and solar heat. Furthermore, the model is easily adaptable to different application cases in DH systems such as operational planning, bidding to electricity markets and long-term evaluation. We present results from three real cases in Denmark with different requirements.

Rainfall Variability, Risk, and Resilience: A Comparative Study of Insured vs. Non-Insured Farmers in Maharashtra, India

This paper examines the risks involved in Maharashtra’s agriculture and investigates the mitigation strategies adopted by insured and non-insured farmers in Latur and Pune districts. By using a multi-stage sampling method, 400 sample households were selected from two districts. These households comprise 268 insured farmers and 134 non-insured farmers. The results reveal that both rainfall variability and reductions in crop prices significantly impact agricultural revenues. However, the specific risk that carries the most significant weight may vary depending on an individual’s insurance status. Insured farmers are most vulnerable to the unpredictable nature of rainfall, as insurance typically protects against yield losses but not necessarily against market fluctuations. Conversely, non-insured farmers face a constant threat from declining crop prices, even with a good harvest, as they lack the financial buffer provided by crop insurance. This study suggests that the government should integrate pricing risk with crop insurance to reduce risk in agricultural production. Additionally, the farmers should adopt an integrated approach to pest and disease management. The implementation of a comprehensive crop insurance program is needed to ensure equitable coverage for farmers, facilitating widespread benefits. Additionally, more efforts should focus on mitigation of identified risks such as input costs, market fluctuations, and production uncertainties.

CALIPSO-based aerosol extinction profile estimation from MODIS and MERRA-2 data using a hybrid model of Transformer and CNN

Acquiring aerosol vertical distribution information is crucial to accurately quantify the aerosol radiation effect on climate and understand the environmental pollution mechanism of the atmosphere. Passive remote sensing has shown its capability to gain large-scale, high spatiotemporal resolution aerosol vertical information such as aerosol layer height (ALH). However, it is still challenging to extract detailed aerosol vertical distribution information, e.g., aerosol extinction profile (AEP), from passive observations. To fill this gap, this study proposed a hybrid model of Transformer and convolutional neural network (CNN) to estimate the AEP from passive multispectral remote sensing (MODIS) measurements with the aid of three-dimensional reanalysis data (MERRA-2). Specifically, the model is learned to estimate the AEP, which is called AproNet, by using the active space-borne lidar (CALIPSO) data as supervised information. Besides, we design a shape invariant loss (SIL) to better capture the shape characteristics of the AEP and incorporate an auxiliary scene awareness loss (SAL) to enhance the model's generalization capacity and physical reliability outside the CALIPSO orbit. The extensive quantitative experiments show that the AEPs estimated by the proposed model agree well with the CALIPSO measurements with an overall performance of IOA=0.821, R=0.800, MAE= 0.014, and RMSE= 0.041, respectively. Qualitative comparisons also demonstrate the model's reliability in estimating the aerosol three-dimensional spatial distribution. Independent year test and comparisons with ground-based lidar measurements further indicate the robustness of the proposed model despite some degradation in performance. However, the incompleteness and uncertainty of the CALIOP products limited the performance of the proposed model to some extent. In the future, the model needs to be further physically constrained and strengthened with more data sources to improve reliability. In general, this study paves the way for acquiring aerosol extinction profiles with high spatiotemporal resolution over a large geographical space.

Investigation and prediction of impact of aggregate size and shape on porosity and compressive strength of pervious concrete

ABSTRACT Pervious concrete is proven to environmentally benefit urban pavement construction. Optimising compaction energy evidently reduces uncertainty in the mass production of pervious concrete with uniform characteristics. The distribution of compaction energy in wet mix and rearrangement of binder-coated aggregate particles are affected by the size and shape of aggregates. This study analyses the impact of aggregate size and shape on porosity and compressive strength prediction from mix-design parameters. Aggregate-to-cement ratio (3, 4 and 5), compaction (0, 30 and 60 blows from standard Proctor rammer), aggregate sizes (5–12 mm, 12–18 mm and 18–25 mm) and aggregate shape (0, 200 and 1000 revolutions of milling in Los Angeles Abrasion Value) were used to cast a total number of 486 samples. Constituents were mixed in an electrically powered drum of capacity 96 L, for 20 min. Porosity was computed from constituent ratios (theoretical porosity) and apparent weight (measured porosity). The compressive strength of samples was recorded using a Universal Testing Machine. The measured porosity and theoretical porosity relationship was significantly affected by the shape of the aggregate and not by the size. Modified Ryshkewitch’s model predicted compressive strength from porosity, aggregate-to-cement ratio, compaction and aggregate size and shape with an accuracy of 0.92 (R2). Two of six arbitrary constants depended on aggregate shape and size.

Enhanced Monte‐Carlo tree search for dynamic flexible job shop scheduling with transportation time constraints

AbstractWith the increasing uncertainty and complexity of industrial production, dynamic events are inevitably happened during the production process. This paper deals with the transportation time constrained dynamic flexible job shop scheduling problem with machine breakdown, new jobs arrival, jobs cancellation, and processing time change of operations. Considering the actual production situation, a makespan rescheduling model based on transportation time is established. A Monte‐Carlo tree search based algorithm is proposed to solve the dynamic flexible job shop scheduling problem such that the makespan is minimized. In order to improve the accuracy of the developed algorithm and the quality of the obtained scheduling schemes, subtree keeping policy, multi‐branching simulation, reinforcement learning, greedy strategy, and expectation‐best evaluation method are employed. Moreover, a subtree pruning policy is embedded to improve the search efficiency of the developed algorithm. Simulation experiments are conducted on a series of instances of different sizes to compare the developed approach with the GA‐based scheduling approach and the Monte‐Carlo tree search based approach. The simulation results indicate that the developed approach is superior to the existing scheduling approaches in terms of the qualities of the obtained solutions.

Enhancing Evapotranspiration Estimations through Multi-Source Product Fusion in the Yellow River Basin, China

An accurate estimation of evapotranspiration (ET) is critical to understanding the water cycle in watersheds and promoting the sustainable utilization of water resources. Although there are various ET products in the Yellow River Basin, various ET products have many uncertainties due to input data, parameterization schemes, and scale conversion, resulting in significant uncertainties in regional ET data products. To reduce the uncertainty of a single product and obtain more accurate ET data, more accurate ET data can be obtained by fusing different ET data. Addressing this challenge, by calculating the uncertainty of three ET data products, namely global land surface satellite (GLASS) ET, Penman–Monteith–Leuning (PML)-V2 ET, and reliability-affordable averaging (REA) ET, the weight of each product is obtained to drive the Bayesian three-cornered Hat (BTCH) algorithm to obtain higher quality fused ET data, which are then validated at the site and basin scales, and the accuracy has significantly improved compared to a single input product. On a daily scale, the fused data’s root mean square error (RMSE) is 0.78 mm/day and 1.14 mm/day. The mean absolute error (MAE) is 0.53 mm/day and 0.84 mm/day, respectively, which has a lower RMSE and MAE than the model input data; the correlation coefficients (R) are 0.9 and 0.83, respectively. At the basin scale, the RMSE and MAE of the annual average ET of the fused data are 11.77 mm/year and 14.95 mm/year, respectively, and the correlation coefficient is 0.84. The results show that the BTCH ET fusion data are better than single-input product data. An analysis of the fused ET data on a spatiotemporal scale shows that from 2001 to 2017, the ET increased in 85.64% of the area of the Yellow River Basin. Fluctuations in ET were greater in the middle reaches of the Yellow River than in the upstream and downstream regions. The BTCH algorithm has indispensable reference value for regional ET estimation research, and the ET data after BTCH algorithm fusion have higher data quality than the original input data. The fused ET data can inform the development of management strategies for water resources in the YRB and provide a deeper understanding of the regional water supply and demand balance mechanism.

Robots for the Energy Transition: A Review

The energy transition relies on an increasingly massive and pervasive use of renewable energy sources, mainly photovoltaic and wind, characterized by an intrinsic degree of production uncertainty, mostly due to meteorological conditions variability that, even if accurately estimated, can hardly be kept under control. Because of this limit, continuously monitoring the operative status of each renewable energy-based power plant becomes relevant in order to timely face any other uncertainty source such as those related to the plant operation and maintenance (O&amp;M), whose effect may become relevant in terms of the levelized cost of energy. In this frame, the use of robots, which incorporate fully automatic platforms capable of monitoring each plant and also allow effective and efficient process operation, can be considered a feasible solution. This paper carries out a review on the use of robots for the O&amp;M of photovoltaic, wind, hydroelectric, and concentrated solar power, including robot applications for controlling power lines, whose role can in fact be considered a key complementary issue within the energy transition. It is shown that various robotic solutions have so far been proposed both by the academy and by industries and that implementing their use should be considered mandatory for the energy transition scenario.

Characterizing the uncertainty of CMORPH products for estimating orographic precipitation over Northern California

Satellite-based precipitation products (SPPs), such as the NOAA Climate Prediction Center (CPC) Morphing technique (CMORPH), have greatly extended our ability to monitor global precipitation. However, their performance over complex terrain remains highly uncertain. To improve accuracy, CPC has recently upgraded its operational SPP to the second generation CMORPH–CMORPH2. In addition to such efforts, the reliability of SPPs can be further enhanced by developing robust uncertainty estimates. This study employs a censored, shifted gamma distribution (CSGD)-based error modeling framework to develop error models for both CMORPH2 and its predecessor, CMORPH1, at a 6-hourly scale over Northern California—a typical complex terrain region that challenges most SPPs. Using the Stage IV reference precipitation, the relative improvements of CMORPH2 over CMORPH1 are quantified by comparing their CSGD-derived error statistics. The comparison shows that CMORPH2 outperforms CMORPH1 by reducing the overall bias and detection errors, and by better capturing the orographic gradients of precipitation over the Coast Ranges and Sierra Nevada. Validation results show that the trained error models can appropriately represent the bias and random errors; however, the models for both CMORPH2 and CMORPH1 show reduced skills in the high-altitude Sierra Nevada. With high-resolution regional climate simulations, the uncertainty estimates are further improved by incorporating them as covariates. The simulated precipitation shows substantial improvement of the uncertainty estimates over most areas, including the challenging high-altitude Sierra Nevada. Following precipitation, the simulated integrated water vapor transport (IVT) and convective available potential energy (CAPE) also offer modest improvements, mainly along the coast. Independent verification further demonstrates the robustness of the uncertainty estimates during heavy precipitation events. As NOAA is currently reprocessing CMORPH2 to produce an updated global precipitation record from 1991 onward, this error modeling framework holds potential for broader applications in quantifying the uncertainty of CMORPH2 for estimating orographic precipitation over extended periods and locations.

Exploring resource recovery from diverted organics: Life cycle assessment comparison of options for managing the organic fraction of municipal solid waste

Diversion of the organic fraction of municipal solid waste (OFMSW) from landfills is increasing. Previous life cycle assessment studies have evaluated subsets of OFMSW management options, but conclusions are inconsistent, and none have evaluated diverse applications of material by-products. The primary objective of this work was to identify sustainability-based improvements to the selection, design, implementation, and operation of organics waste diversion management technologies. Process modeling and life cycle assessment were used to compare OFMSW composting, anaerobic digestion, and pyrolysis, with biochar used as a landfill cover, leachate treatment sorbent, and land applicant. Material and energy flows, calculated by newly developed models for the defined functional unit (1 kg MSW over a 20-year timeframe), were translated to environmental performance using ecoinvent and USLCI databases and TRACI method. Additionally, uncertainty, sensitivity, and scenario analyses were conducted to evaluate the implications of model uncertainties, design decisions, and resource recovery tradeoffs. OFMSW pyrolysis usually (65 % of uncertainty assessment simulations) had the best global warming performance mostly due to energy recovery and biochar's carbon sequestration benefit, which was independent of fate. Pyrolyzing the biosolids from OFMSW anaerobic digestion recovered the most energy and had the best performance in 34 % of uncertainty simulations. Material recovery amounts were large (e.g., more biochar was produced than required for novel uses) and warrant feasibility considerations. Global warming performance was more sensitive to uncertainty in carbon sequestration and primary energy production than in fertilizer offset, energy conversion, or heat offset approach. Practical implications include the potential for biochar supply to outweigh demand, and inconsistent revenue from the sale of recovered energy and carbon credits; future research could focus on evaluating the relative social and economic sustainability of the OFMSW management technologies.