Precipitation Type Research Articles

Climatic characteristics and main weather patterns of extreme precipitation in the middle Yangtze River valley

Abstract Based on the daily precipitation data and ERA5 reanalysis data of 40 years from 1981 to 2018 in the middle Yangtze River Valley (MYRV), the climatic characteristics of extreme precipitation are analyzed using statistical methods. The multivariate empirical orthogonal functions and spectral clustering methods are used to classify and synthesize the extreme precipitation weather. The results show that: (1) The spatial distribution of the extreme precipitation threshold is uneven due to the regional topography. The spatial distribution of the average precipitation and frequency of extreme precipitation days is characterized by the north-south antiphase distribution. (2) According to the main influencing systems, the 215 regional extreme precipitation days in the MYRV in the past 40 years can be classified into three types: southwest vortex type, typhoon type, and cold trough shear line type. (3) The southwest vortex type of extreme precipitation occurs in the deep warm and humid airflow in front of the southwest vortex trough, but the typhoon type has better thermal dynamic conditions, and the cold and warm airflow convergence of the cold trough shear line type is more obvious. The rainfall area of three types of extreme precipitation is the result of the synergistic effect of the system.

Effects of coal mining and climate-environment factors on the evolution of a typical Eurasian grassland

Coal mining can significantly impact vegetation evolution, yet the limited information on its patterns and driving factors hampers efforts to mitigate these effects and reclaim abandoned mines. This study aimed to 1) examine vegetation evolution in a semiarid steppe watershed in northeast China; and 2) characterize the driving factors behind this evolution. We analyzed the impact of twelve selected driving factors on fractional vegetation coverage (FVC) from 2000 to 2021 using a dimidiate pixel model, Sen's slope analysis, Mann-Kendall trend test, coefficient of variation analysis, and Geodetector model. At a significance level of α = 0.05, our findings revealed a south-to-north decline pattern in FVC, a significant decrease trend in proximity to coal mines, and a notable increase trend adjacent to river channels. Approximately 37% of the watershed exhibited low FVC, while the overall temporal trend across the watershed was deemed insignificant. Areas surrounding the mines experienced a substantial reduction in FVC due to coal mining activities, while FVC variations across the watershed were linked to precipitation, temperature, and soil type. FVC predictions improved notably when interactions between multiple two-way factors were considered. Each driving factors displayed an optimal range (e.g., precipitation = 63–71 mm) for maximizing FVC. Given the study watershed's status as a national energy base, understanding vegetation responses to coal mining and climate-environment changes is crucial for sustaining fragile terrestrial ecosystems and socioeconomic development. Achieving a long-time balance between coal extraction and ecological protection is essential. The study outcomes hold significant promise for advancing ecological conservation, vegetation restoration, and mitigation of environmental degradation in semiarid regions affected by extensive coal mining and climate fluctuations. These findings contribute to the strategic management of such areas, promoting sustainable practices amidst evolving environmental challenges.

Factors Affecting the Spatiotemporal Variation of Precipitation in the Songhua River Basin of China

The study aimed to investigate the spatiotemporal variation of annual precipitation and extreme precipitation within the Songhua River Basin (SRB). It utilized precipitation data collected from 60 meteorological stations within the SRB during the period 1968–2019. Employing Empirical Orthogonal Function (EOF) analysis, it decomposed spatiotemporal characteristics of annual precipitation in the SRB. Through Pearson correlation analysis, application of the cross-wavelet transform, and wavelet coherence analysis, the current study explored the correlation between geographical factors, local air temperature, circulation factors, and annual and extreme precipitation. The results indicated an increasing trend for annual precipitation and for most indices of extreme precipitation within the SRB, apart from the consecutive dry days (CDD). Spatially, a general pattern of “more in the east and less in the west” was observed. Annual precipitation types in the basin were resolved into two modes with the first mode showing a general tendency of more (or less) precipitation over the entire basin, while the second mode exhibited less (or more) precipitation in the western areas and more (or less) in the eastern areas. Longitude, latitude, and altitude significantly impacted annual precipitation and extreme precipitation. Local air temperature notably affected the consecutive wet days (CWD). The West Pacific Subtropical High (WPSH) exerts a strong influence on the annual precipitation and extreme precipitation within the basin.

Quantitative study on the microstructural evolution and dimensional stability mechanism of 2024 Al alloy during long-term thermal cycling

During the start-stop process of high-precision instruments in service, the critical materials of instruments undergo thermal cycling, resulting in changes in microstructure and dimension. In this paper, the evolution and coupling effects of dislocations and precipitates within 2024 Al alloy during 500 cycles were investigated, and their influence on the dimensional change was quantitatively characterized and decoupling analyzed. Transmission Electron Microscopy (TEM)/aberration-corrected scanning TEM (CS-TEM), X-ray diffraction (XRD), and Three-Dimensional Atom Probe Tomography (3D-APT) were used for microstructural evolution analysis and quantitative statistics. The dislocation density increased from 3.32 × 1014 m−2 to 5.75 × 1014 m−2 after 500 cycles, which accelerated elemental diffusion and provided nucleation sites of precipitates, contributing to an increase in the number density of the S'/S phase from 1.04 × 1024 m−3 to 1.41 × 1024 m−3. Based on the quantitative statistics of precipitate types and corresponding volume fractions, the dimensional change induced by precipitate evolution was −1.25 × 10−4. The dimensional change caused by the free volume introduced by dislocation density was 3.32 × 10−6. Comparing these values with the experimental value of −1.94 × 10−4, it is clear that the precipitate evolution is the main factor that triggers the dimensional change.

Minimization of residues generated during the operation of treatment facilities

The activity of sewage treatment plants for the treatment of domestic wastewater is not limited only to wastewater treatment. The main problem in this activity is the treatment and disposal of the resulting sediments at sewage treatment plants, where the main task is to reduce the volume of sediment and then transform it into a useful product that does not cause environmental pollution. Currently, representatives of landfills refuse to accept this type of precipitation, explaining that this type of precipitation does not belong to the category of solid municipal waste. The issue of using sediments from sewage treatment plants as raw materials for manufacturing remains open, but in our opinion this type of sediment can be recycled in wastewater treatment technology. The practical value lies in conducting industrial tests of the converter of sewage sludge into liquid effluents, including all waste generated during the operation of sewage treatment plants with subsequent post-treatment of liquid effluents at wastewater treatment plants. These tests will reduce the amount of sediment by up to 90%, which will minimize the intensive accumulation of precipitation from sewage treatment plants and improve the state of the environment.

Response of vegetation to SPI and driving factors in Chinese mainland

Considering the sensitivity and vulnerability of vegetation to precipitation changes, exploring the response of vegetation to standardized precipitation index (SPI) is essential for ecosystem stability. Based on observed data from 624 stations and Moderate Resolution Imaging Spectroradiometer (MODIS) dataset from 2000 to 2018, Spearman's rank correlation coefficient (rs) of SPI and vegetation condition index (VCI) in Chinese mainland was analysed, response time of vegetation to SPI was investigated, and random forest models were constructed to explore the drivers of vegetation response. The results revealed that: (1) SPI and VCI exhibit a loop distribution. The correlation between VCI and 1-month SPI is mostly positive, accounting for 77.58% of the total; rs shows an increasing-decreasing distribution pattern, and the response time of VCI to 1-month SPI indicates an overall decreasing-increasing-decreasing trend. (2) Precipitation, temperature, altitude, vegetation, land use, landform and soil types all have influence on the correlation between 1-month SPI and VCI. Under various factors, rs is concentrated in the range of -0.1 ∼ 0.2, and the response time of VCI to SPI under various factors primarily falls into the ranges of 0 ∼ 1 month and 6 ∼ 9 months. Meanwhile, rs generally decreases with the increasing latitude, ranging between -0.1 and 0.2, whereas the mean response time increases with latitude, within a 0 ∼ 6 months range. (3) Precipitation, temperature and altitude are the top three important factors that affect both maximum rs of VCI and 1-month SPI and response time of VCI to 1-month SPI. The rs is positive in areas with low precipitation, high temperature and low altitude, whereas response time is generally long in areas with low precipitation, low temperature and high altitude. This study is significant for better understanding the interactions between vegetation and SPI changes, and provides reference for vegetation irrigation and vegetation management.

On the Distribution of Convective and Stratiform Precipitation in Tropical Cyclones from Airborne Doppler Radar and Its Relationship to Intensity Change and Environmental Wind Shear Direction

Abstract Airborne Doppler radar reflectivity data collected in hurricanes on the NOAA P-3 aircraft between 1997 and 2021 were parsed into different modes of precipitation: stratiform precipitation, shallow convection, moderate convection, and deep convection. Stratiform precipitation was the most frequent precipitation mode with 82.6% of all observed precipitation while deep convection was the most infrequent at 1.3%. When stratified by 12-h intensity change, intensifying TCs had a greater areal coverage of total convection in the eyewall compared to weakening and steady-state TCs. The largest difference in the azimuthal distributions in the precipitation modes was in deep convection, which was mostly confined to the downshear-left quadrant in weakening and steady-state hurricanes and more symmetrically distributed in intensifying hurricanes. For all intensity change categories, the most symmetrically distributed precipitation mode was stratiform rain. To build upon the results of a recent thermodynamic study, the precipitation data were recategorized for hurricanes experiencing deep-layer wind shear with either a northerly component or southerly component. Like intensifying storms, hurricanes that experienced northerly component shear had a more symmetric distribution of deep convection than southerly component shear storms, which had a distribution of deep convection that resembled weakening storms. The greatest difference in the precipitation distributions between the shear direction groups were in major hurricanes experiencing moderate (4.5–11 m s−1) wind shear values. Consistent with previous airborne radar studies, the results suggest that considering the distribution of deep convection and the thermodynamic distributions associated with differing environmental wind shear direction could aid TC intensity forecasts. Significance Statement This research investigates how the distribution of different types of precipitation are related to tropical cyclone (TC) intensity change. Even though deep convection—the tallest clouds—is the least frequent type of precipitation, it has the strongest relationship to intensity change with uniform distributions around the eyewall associated with intensification. Less significant relationships were noticed for shallower clouds and stratiform (lighter) rain. The study also analyzed how change in direction of the large-scale winds with height (wind shear) influences intensity change. When wind shear is northerly, there is a more symmetric distribution of deep convection compared to when wind shear is southerly. These relationships illustrate how wind shear direction influences TC convective structure and, in turn, TC intensity change.

Dispersed Relativistic Electron Precipitation Patterns Between the Ion and Electron Isotropy Boundaries

AbstractRelativistic electron precipitation to the Earth's atmosphere is an important loss mechanism of inner magnetosphere electrons, contributing significantly to the dynamics of the radiation belts. Such precipitation may be driven by electron resonant scattering by middle‐latitude whistler‐mode waves at dawn to noon; by electromagnetic ion cyclotron (EMIC) waves at dusk; or by curvature scattering at the isotropy boundary (at the inner edge of the electron plasma sheet anywhere on the nightside, from dusk to dawn). Using low‐altitude ELFIN and near‐equatorial THEMIS measurements, we report on a new type of relativistic electron precipitation that shares some properties with the traditional curvature scattering mechanism (occurring on the nightside and often having a clear energy/L‐shell dispersion). However, it is less common than the typical electron isotropy boundary and it is observed most often during substorms. It is seen equatorward of (and well separated from) the electron isotropy boundary and around or poleward of the ion isotropy boundary (the inner edge of the ion plasma sheet). It may be due to one or more of the following mechanisms: EMIC waves in the presence of a specific radial profile of the cold plasma density; a regional suppression of the magnetic field enhancing curvature scattering locally; and/or electron resonant scattering by kinetic Alfvén waves.

Assessment of precipitation type discrimination methods on glacier of Qilian Mountains

Assessment of precipitation type discrimination methods on glacier of Qilian Mountains

Precipitation Vertical Structure Characterization: A Feature-Based Approach

Abstract The three-dimensional (3D) structure of precipitation systems is highly dependent on hydrometeor formation processes and microphysics. This study aims to characterize distinct vertical profiles of precipitation regimes by relying on the availability of a high-quality, spatially dense radar network and its capability to observe the 3D structure of the storms. A deep-learning-based framework, coupled with unsupervised clustering methods, is developed to identify types of precipitation structures irrespective of their physical properties. A 6-month period of 3D reflectivity profiles from the Multi-Radar Multi-Sensor (MRMS) network is used to identify different regimes and investigate their properties with respect to the underlying environmental conditions. Dominant features retrieved from radar reflectivity profiles using convolutional neural-network-based autoencoders are employed to identify similar-looking vertical structures using coupled k-means and agglomerative clustering algorithms. The k-means method identifies distinct groups, while the agglomerative clustering visualizes intercluster relationships. The framework identifies 18 clusters that can be broadly combined into five groups of varied echo-top heights. The 18 clusters demonstrate variability with respect to structural features and precipitation rate/type, implying that profiles in each group belong to a physically different precipitation regime. An independent analysis of the regime properties is conducted by matching the MRMS reflectivity profiles with environmental parameters derived from the High-Resolution Rapid Refresh model forecasts. The distribution of the environmental variables confirms cluster-specific feature properties, confirming the physics-based regime separation across the clusters and their dependence on the vertical structure. The identified precipitation regimes can assist in developing physics-guided retrievals and studying precipitation regimes. Significance Statement This study proposes a systematic model to identify precipitation profiles of distinct vertical structures and evaluate their dependence on environmental conditions. The model was developed using ground-based radar observations; however, there is potential to extend this model to reflectivity profiles from both ground- and satellite-based sensors. In addition, the identified precipitation regime clusters could be a proxy for the vertical structure of precipitation systems and assist in determining the structural variability within traditional precipitation type classification (e.g., convective versus stratiform). Moreover, identifying the precipitation regimes could also be used to improve satellite-based precipitation retrievals. Finally, a better understanding of precipitation structure would also help improve the initialization of climate models.

Classification of precipitation types in Poland using machine learning and threshold temperature methods

The phase in which precipitation falls—rainfall, snowfall, or sleet—has a considerable impact on hydrology and surface runoff. However, many weather stations only provide information on the total amount of precipitation, at other stations series are short or incomplete. To address this issue, data from 40 meteorological stations in Poland spanning the years 1966–2020 were utilized in this study to classify precipitation. Three methods were used to differentiate between rainfall and snowfall: machine learning (i.e., Random Forest), daily mean threshold air temperature, and daily wet bulb threshold temperature. The key findings of this study are: (i) the Random Forest (RF) method demonstrated the highest accuracy in rainfall/snowfall classification among the used approaches, which spanned from 0.90 to 1.00 across all stations and months; (ii) the classification accuracy provided by the mean wet bulb temperature and daily mean threshold air temperature approaches were quite similar, which spanned from 0.86 to 1.00 across all stations and months; (iii) Values of optimized mean threshold temperature and optimized wet bulb threshold temperature were determined for each of the 40 meteorological stations; (iv) the inclusion of water vapor pressure has a noteworthy impact on the RF classification model, and the removal of mean wet bulb temperature from the input data set leads to an improvement in the classification accuracy of the RF model. Future research should be conducted to explore the variations in the effectiveness of precipitation classification for each station.

Influence of Mo Content on the Precipitation Behavior of 13Ni Maraging Ultra-High Strength Steels

This study offers valuable insights into the precipitation behavior of 13Ni maraging steels, emphasizing the role of molybdenum content in their microstructure, strengthening, and precipitate evolution. Precipitate morphology and crystallography were examined using a combination of high-resolution transmission electron microscopy and selected area electron diffraction. Strengthening mechanisms were assessed through Vickers hardness measurements. All the examined samples exhibited a lath martensite microstructure and displayed an increasing hardness over the aging time. The molybdenum content not only influenced the presence of retained austenite in the initial microstructure but also affected the type of precipitates formed during the early aging stages. Initially, Ni3Mo precipitates were formed, succeeded by the formation of more stable Fe2(Mo,Ti) Laves precipitates. The ultra-high strength of 13Ni maraging steels arises from the combination of the precipitate type and size distribution. The base composition of 13Ni maraging steels achieved a peak hardness of 798 HV1 through the precipitation of Laves Fe2(Mo,Ti) phases ranging from 3 to 14 nm in diameter.

Metal cation concentrations improve understanding of controls on soil organic carbon across a precipitation by vegetation gradient in the Patagonian Andes

Tephra-derived soils retain more organic carbon (C) than soils formed from any other parent material, but this C may be sensitive to changes in climate and land use. Here we evaluate the effects of precipitation, temperature, and afforestation on extractable metals and organic C storage in young tephra-derived soils in a temperate climate. We conducted our investigation across five sites in the Patagonian Andes that vary from 250 mm to 2200 mm mean annual precipitation, and 12 to 9.7 ℃ mean annual temperature from east to west. At each of the five sites are paired plots of natural vegetation, varying from grasses and shrubs at the dry sites to closed-canopy forest at the wet, and stands of Pinus ponderosa planted in monocultures 35 years prior to sampling. Previous research at these sites showed that aboveground net primary production and soil organic C increased with rainfall, but total soil organic C content was lower in pine plantations than natural vegetation. Here we assess whether variation in precipitation and vegetation type also affect soil mineral properties that promote soil C stabilization. Soils were collected to the depth of auger refusal and extracted with 0.5 M HCl for 24 h to target the combined exchangeable and adsorbed metals, including secondary short-range-ordered mineral phases and the plant available pools of Mg, Ca, and K. Pine afforestation lowered concentrations of HCl-extractable K (p < 0.1) and Ca (p < 0.01) within the top 0 – 30 cm. Other elements, while not affected by vegetation type, did respond to the rainfall gradient. Al, Si, P, and Mn all increased in the surface soils with increasing rainfall (p < 0.01), suggesting the development of short-range-order volcanic mineral phases that retain nutrients such as P and Mn. The addition of Al and Ca in the linear model to describe soil organic C explained more of the total variance than rainfall and vegetation type alone, indicating the importance of Al complexes and cation (Ca) bridging with secondary minerals to soil C retention. Importantly, the lower concentration of Ca in planted pine soils may signal a long-term decrease in the potential soil C stored in afforested soils due to a lower capacity for cation bridging. Our results show that the chemistry of these young tephra soils is dynamic, responding to both precipitation and afforestation in distinct ways with potential long-term impacts on nutrient cycling and C storage.

Quantifying the ecological carrying capacity of grasslands in Inner Mongolia.

Quantifying the ecological carrying capacity has emerged as a crucial factor for maintaining ecosystem stability for sustainable development in vulnerable eco-regions. Here, we propose a new framework for ecological carrying capacity quantification suitable for vulnerable eco-regions. We applied this framework to calculate the ecological carrying capacity of Inner Mongolia from 1987-2015 and used a geographical detector to identify the driving factors behind spatial heterogeneity. Our results revealed the following. (1) The above-ground net primary production (ANPP) required to support the ecosystem service of soil conservation (ANPPSC) decreased from northeast to southwest, whereas the distribution pattern of ANPP required to support the ecosystem service of sand fixation (ANPPSF) exhibited a contrary trend. The average annual ANPP required to support the ecosystem service of natural regeneration (ANPPNR) in Inner Mongolia from 1987 to 2015 was 101.27 gCm-2year-1, revealing a similar spatial distribution with ANPP. (2) The total ecological carrying capacity of Inner Mongolian grassland was 78.52 million sheep unit hm-2. The regions with insufficient provisioning service capability accounted for 4.18% of the total area, primarily concentrated in the east and northwest. (3) The average optimal livestock number for grasslands in Inner Mongolia was 1.59 sheep unit hm-2 from 1987-2015, ranging from 0.77 to 1.69 sheep unit hm-2 across different zones. The average ecological carrying capacity of the cold temperate humid, medium-temperate arid, and warm temperate semi-humid regions was less than 1.08 sheep unit m-2, suggesting a need to prohibit grazing in these areas. (4) The primary influencing factors affecting ecological carrying capacity distribution were normalized difference vegetation index (NDVI), precipitation, and soil type. The framework developed herein can help identify sustainable development potential from the ecosystem service perspective and effectively contribute to decision-making in grassland ecosystem management.

Quantifying patch size distributions of forest disturbances in protected areas across the European Alps

AbstractAimNatural disturbances are key drivers of forest ecosystem dynamics and are highly sensitive to global change. Despite their importance, central disturbance characteristics remain unknown for many forests worldwide. Here, we quantified an important component of the forest disturbance regime—the distribution of patch sizes—in strictly protected areas by asking: (i) How are patch sizes of naturally occurring disturbances distributed across the Alps and how can they best be quantified? (ii) Are patch size distributions stochastic or can they be explained by environmental drivers? (iii) What are the return periods of extreme disturbance events?LocationEuropean Alps.MethodsWe analysed satellite‐based disturbance maps for the period 1986–2020 across a network of 12 strictly protected areas, modelling patch sizes of all observed disturbance patches as well as of annual extreme events. We tested the influence of temperature, precipitation, topographic complexity and forest type on patch size distributions.ResultsDisturbance patch sizes across the Alps (median 0.36 ha, 5th percentile 0.18 ha and 95th percentile 1.71 ha) as well as their annual extremes (0.72 ha, 0.18–7.11 ha) are best described by a Fréchet distribution. The size of annual extreme events significantly increased with intra‐annual temperature amplitude (+0.98 ha with a one standard deviation increase) and the share of evergreen trees (+0.63 ha). On average, disturbance patches of 5.5 ha (95% credible interval 2.6–17.5 ha) occur once every 30 years, whereas patches of 2.6 ha (1.2–7.0 ha) occur once every 10 years.Main ConclusionsDisturbances caused by natural agents are generally small and stochastic across the Alps. Extreme events are driven by climate, suggesting sensitivity of disturbance patch sizes to climate change. Our results provide a baseline for monitoring climate‐induced changes in forest disturbance regimes, and provide important information for the management and conservation of forest ecosystems.

Characterization and Exploration of Placket-Burman-Designed Porous Calcium Carbonate (Vaterite) Microparticles.

The objective of the research was to identify significant variables that impact the porosity-related properties of CaCO3 particles. The Placket-Burman design was employed to screen multiple variables, including pH, molar concentrations of calcium chloride and sodium carbonate, temperature, concentration of Gelucire 44/14, Cremophor RH40, Solutol HS15, Labrasol, mixing rate, reaction time, and order of addition. The response variables were surface area, pore radius, and pore volume. Influential methodologies such as XRD, FTIR, Raman spectroscopy, and TGA were utilized to validate the precipitate type. The BET surface area ranged from 1.5 to 16.14 m2/g, while the pore radius varied from 2.62 to 6.68 nm, and the pore volume exhibited a range of 2.43 to 37.97 cc/gm. Vaterite structures with spherical mesoporous characteristics were observed at high pH, whereas calcite formations occurred at low pH. The order of addition impacted the surface area but did not affect the pore volume. To maximize the surface area, a lower reaction time and molar concentrations of sodium carbonate were found to be advantageous. The pore radius was influenced by the pH, surfactants, and reaction conditions. The sediments were categorized based on the percentage of vaterite formation. The instrumental techniques effectively characterized the precipitates and provided a valuable complementary analysis.

Ionospheric Plasma Density Gradients Associated With Night‐Side Energetic Electron Precipitation

AbstractEnergetic electron precipitation from the equatorial magnetosphere into the atmosphere plays an important role in magnetosphere‐ionosphere coupling: precipitating electrons alter ionospheric properties, whereas ionospheric outflows modify equatorial plasma conditions affecting electromagnetic wave generation and energetic electron scattering. However, ionospheric measurements cannot be directly related to wave and energetic electron properties measured by high‐altitude, near‐equatorial spacecraft, due to large mapping uncertainties. We aim to resolve this by projecting low‐altitude measurements of energetic electron precipitation by ELFIN CubeSats onto total electron content (TEC) maps serving as a proxy for ionospheric density structures. We examine three types of precipitation on the nightside: precipitation of &lt;200 keV electrons in the plasma sheet, bursty precipitation of &lt;500 keV electrons by whistler‐mode waves, and relativistic (&gt;500 keV) electron precipitation by EMIC waves. All three types of precipitation show distinct features in TEC horizontal gradients, and we discuss possible implications of these features.

Global sensitivity analysis of APSIM-wheat yield predictions to model parameters and inputs

The performance of cropping system models is often limited by uncertainties in model parameters and inputs. This study aims to explore how model yield prediction responds to these uncertainties under various environmental and management conditions. The Sobol’ method was used to investigate the sensitivity of the Agricultural Production Systems SIMulator (APSIM)-Wheat yield prediction to the following factors: air temperature (maximum and minimum), precipitation, initial soil nitrogen content, nitrogen fertilization amount, and soil hydraulic parameters. Eighteen scenarios were defined to consider a combination of three weather conditions (wet, normal, and dry growing season, based on the historical climatology of the Wimmera district of Victoria, Australia), three soil types (sandy, loamy, and clayey soils), and two nitrogen fertilization applications (50 and 100 kg N/ha). Eight thousand APSIM simulations were used to calculate the first order and total sensitivity indices for each scenario. The effects of weather, soil water characteristics and nitrogen availability were estimated by measuring their impacts on sensitivity indices. Our results show that yield was more sensitive either to variables that control water availability (precipitation and soil type) or variables that control nitrogen availability, depending on which was more limiting to wheat growth under the scenario. For example, in case of low nitrogen fertilization scenario, the initial nitrogen content sensitivity ranked first. Variation of this factor contributed 64% to 79% to the variance in simulated yield for clayey soils, nearly four times higher than the second-ranked factor (nitrogen fertilization amount). Soil hydraulic parameters and precipitation were most important when the crop growth was more constrained by water availability than by nitrogen availability. In the case of sandy soils in dry years with high nitrogen fertilization level, soil parameters and precipitation accounted for 83% of the yield variability. Minimum temperature consistently ranked as the least important factor under all scenarios. This work will help researchers better understand the model inputs’ impacts on the simulated yield variability under various soil, management, and weather conditions. The results also support agronomists and practitioners in the optimal use of the APSIM-Wheat model as a management tool.

A Statistical Forecast Model for Extratropical Cyclones Including Intensity and Precipitation Type

Abstract Winter extratropical cyclones (ETCs) are dominant features of winter weather on the east coast of North America. These storms are characterized by high winds and heavy precipitation (rain, snow, and ice). ETCs are well predicted by numerical weather prediction models (NWPs) at short- to midrange forecast lead times, but prediction on seasonal time scales is lacking. We develop a set of multiple linear regression models, using stepwise regression and cross validation, to predict the number of storms expected to affect a specific location throughout the winter storm season. Each model in the set predicts a specific storm type (e.g., snow, rain, or bomb storms). This set of models is applied in a probabilistic forecast framework that uses the probability density function of the prediction in combination with climatological mean storm activity. The resulting forecast makes statements about the likelihood of below-average, average, or above-average activity for all storms and for each of the type-specific subsets of storms. Though this forecast framework could in theory be applied anywhere, we demonstrate its skill in forecasting the characteristics of the winter storm season experienced in Halifax, Nova Scotia, Canada. Significance Statement Winter storms are a disruptive but inevitable part of life on the eastern coast of North America all the way from the Carolinas to Labrador. Knowing each fall what to expect for the upcoming winter storm season is not only a matter of public interest, but also of great public safety and financial importance. Here we develop a model that uses the state of the atmosphere over the month of September to forecast the upcoming winter storm characteristics for a specified region of interest. Our model uses a multiple linear regression approach to make skilled forecasts including probability statements about the level and type of storm activity. Forecasts can be used to inform planning for the winter ahead.

The effect of nickel on the carbide precipitation behavior in Cr–Mo–V hot-working die steel

The addition of small amount of nickel to hot-working die steel has been found to be beneficial for hardenability and toughness. In this paper, the addition of nickel to Cr–Mo–V hot-working die steel was studied to improve hardenability, and the inherent mechanism of enhancing toughness was further explored. The research results indicate that nickel as a non-carbide-forming element does not alter the type and total amount of carbide precipitation at equilibrium, but it can dissolve into the matrix and form substitutional lattice distortions with Fe atoms, promoting the dispersion of secondary carbides during the tempering process. This mechanism can reduce intergranular precipitation and inhibit grain boundary embrittlement, thereby improving toughness. However, it can also accelerate the precipitation, aggregation, and growth of secondary carbides during prolonged tempering, which deteriorates the high-temperature anti-softening properties of die steels. The role of Ni has been revealed through thermodynamic and kinetic calculations. The results show that Ni significantly enhances the diffusion coefficient of Cr atoms in the face centered cubic (fcc) Fe matrix during tempering and also improves the phase transition driving force of various precipitates. This work provides new insights into the mechanism of Ni in hot-working die steels, and can provide guidance for designing novel hot-working die steel.