Precipitation Stations Research Articles

The Effect of Final Cooling Temperature on Nano Cu Precipitation in a Cu-Bearing High-Strength Low-Alloy Steel

Nano Cu precipitation plays a crucial role in significantly improving the performance of the Cu-bearing high-strength low-alloy steel. The final cooling temperature effects the transformation products of austenite during the continuous cooling process, as well as the nano precipitations of steel. This study investigated the microstructure and hardness at different final cooling temperatures (750, 700, 650, 600, 550, and 500 °C) using the MMS-300 thermal simulation experimental machine (Northeastern University, Shenyang, China) and Vickers hardness tester. The changes in microstructure and the phase transformation law of austenite were determined during continuous cooling and then analyzed. The precipitation reaction of nano Cu precipitation during continuous cooling was studied using transmission electron microscopy (TEM), revealing the precipitation state under different final cooling temperature conditions. The results showed that the precipitations led to an increase and then a decrease in the microhardness, and the microhardness reaches its peak at 550 °C. The precipitations changed from spherical to elliptical, and the size gradually increased when the final cooling temperature increased.

Dynamical imprints on precipitation cluster statistics across a hierarchy of high-resolution simulations

Abstract. Tropical precipitation cluster area and intensity distributions follow power laws, but the physical processes responsible for this macroscopic behavior remain unknown. We analyze global simulations at 10 km horizontal resolution that are configured to have drastically varying degrees of realism, ranging from global radiative–convective equilibrium to fully realistic atmospheric simulations, to investigate how dynamics influence precipitation statistics. We find the presence of stirring and large-scale vertical overturning, as associated with substantial planetary- and synoptic-scale variability, to be key to having cluster statistics approach power laws. The presence of such large-scale dynamics is reflected in steep vertical velocity spectra. Large-scale rising and sinking modulate the column water vapor and temperature field, leading to a heterogeneous distribution of moist and dry patches and regions of strong mass flux, in which large precipitation clusters form. Our findings suggest that power laws in Earth's precipitation cluster statistics stem from the robust power laws of atmospheric motions.

Influence of Plastic Deformation on the Precipitation Evolution in the Aluminum Alloys in Friction Stir Welding

The influence of temperature on the precipitation evolution in different zones of friction stir welded (FSW) heat-treatable aluminum alloys has been well investigated. However, the role of plastic deformation in affecting precipitation transformations remains less explored. To isolate the contribution of these factors and specifically assess the role of plastic deformation, an approach combining numerical and physical modeling techniques was used. Welding temperature cycles in the FSW weld zones calculated by means of a 3D finite element model were accurately reproduced using a Gleeble instrument. This approach was implemented under two scenarios such as the reproduction of the influence of temperature alone, and the combined effects of temperature and thermally induced plastic strain. The precipitation states and hardness obtained from these controlled experiments were compared to those observed in actual friction stir welds, providing a deeper understanding of the influence mechanisms at play. The results revealed that deformation significantly influences precipitation formation in the stir zone of both 2024 and 6082 alloys, with this effect extending to the heat-affected zone in the case of the 2024 alloy.

Extreme precipitation analysis in Bengawan Solo

Abstract Climate change can trigger natural disasters, such as floods, drought and extreme heatwave. Large amounts and heavy precipitation might result in floods. Extreme weather that made it difficult or inaccurate to analyze the current weather was one of the causes of the flooding incident in the Bengawan Solo River Area. Analysis of extreme weather and climate change on the Bengawan Solo river is still limited. This study aims to analyze extreme precipitation in Bengawan Solo using a statistical approach. To look for variations in the distribution of precipitation in this area, precipitation data from 25 precipitation stations will be analyzed. This research uses daily precipitation data from 2000 - 2023. The research region’s temporal trends show variation in the results, in which most extreme precipitation indices are highly fluctuating with a slight upward trend during 24 years. It is expected that this research’s findings may aid in classifying regions with heavy precipitation and assist relevant parties in developing disaster mitigation strategies.

Microstructure Characterisation and Modelling of Pre-Forging Solution Treatment of 7075 Aluminium Alloy Using Novel Heating Methods

This study evaluates the effectiveness of these conventional heating methods, commonly adopted in the industry with long durations (typically one hour), in comparison to newer, potentially more efficient approaches such as induction coil heating, infrared module heating, and infrared furnaces that can perform solution heat treatment in significantly shorter times (5 to 20 min). The properties of the edge and centre regions of the solution-treated billets, including the state of precipitates, grain structures, and Vickers hardness, are investigated and compared. Results have shown that the 7075 billets heated by conventional heating methods sufficiently dissolved the stable precipitates, achieving hardness ranging from 137 to 141 HV, in contrast to the benchmark unheated, as-received sample of approximately 70 HV. In the meantime, the induction coil and infrared furnace demonstrate notable effectiveness, achieving hardness between 126 and 135 HV. The average grain sizes in the centre and edge regions for all samples are measured as 3 and 8 µm, respectively. However, the impact of the grain size on the hardness is negligible compared to the impact of the precipitates. Finite element (FE) modelling comparing the slowest heating method—the electric furnace—and the fastest heating method—induction coil heating—reveals the latter could heat the billet up to 450 °C at a rate ten times faster than the electric furnace. This study highlights the potential of novel heating techniques in promoting the efficiency of heat treatment processes for 7075 aluminium alloys.

Forecasting Precipitation Using a Markov Chain Model in the Coastal Region in Bangladesh

This work explores the detailed study of Bangladeshi precipitation patterns, with a particular emphasis on modeling annual rainfall changes in six coastal cities using Markov chains. To create a robust Markov chain model with four distinct precipitation states and provide insight into the transition probabilities between these states, the study integrates historical rainfall data spanning nearly three decades (1994–2023). The stationary test statistic (χ²) was computed for a selected number of coastal stations, and transition probabilities between distinct rainfall states were predicted using this historical data. The findings reveal that the observed values of the test statistic, χ², are significant for all coastal stations, indicating a reliable model fit. These results underscore the importance of understanding the temporal evolution of precipitation patterns, which is crucial for effective water resource management, agricultural planning, and disaster preparedness in the region. The study highlights the dynamic nature of rainfall patterns and the necessity for adaptive strategies to mitigate the impacts of climate variability. Furthermore, this research emphasizes the interconnectedness of climate studies and the critical need for enhanced data-gathering methods and international collaboration to bridge knowledge gaps regarding climate variability. By referencing a comprehensive range of scholarly works on climate change, extreme rainfall events, and variability in precipitation patterns, the study provides a thorough overview of the current research landscape in this field. In conclusion, this study not only contributes to the understanding of precipitation dynamics in Bangladeshi coastal cities but also offers valuable insights for policymakers and stakeholders involved in climate adaptation and resilience planning. The integration of Markov chain models with extensive historical data sets serves as a powerful tool for predicting future rainfall trends and developing informed strategies to address the challenges posed by changing precipitation patterns.

Characterizing Asymptotic Dependence between a Satellite Precipitation Product and Station Data in the Northern US Rocky Mountains via the Tail Dependence Regression Framework With a Gibbs Posterior Inference Approach

ABSTRACTThe use of satellite precipitation products (SPP) allows for precipitation information to be collected nearly globally, but questions remain regarding their ability to reproduce extreme precipitation over mountainous terrain. In this work, we assess the ability of the precipitation estimation from remotely sensed information using artificial neural networks‐climate data record (PERSIANN‐CDR) to capture daily precipitation extremes by comparing PERSIANN‐CDR with corresponding station data in the summer at remote locations in the northern US Rocky Mountains of Wyoming, Idaho, and Montana. The assessment utilizes the regular variation framework from extreme value theory and consists of two parts: (1) evaluating the extent to which PERSIANN‐CDR can capture precipitation extremes through inference on an asymptotic dependence parameter, concluding that the level of asymptotic dependence is moderate throughout the region; (2) developing a tail dependence regression modeling framework and a Gibbs posterior approach for inference to investigate the degree to which elevation and topographic heterogeneity impact the level of asymptotic dependence, finding that the inclusion of a set of meteorological covariates, when combined with the PERSIANN‐CDR output, yields an increased level of asymptotic dependence with station data.

Ensemble Predictability of Week 3/4 Precipitation and Temperature over the United States via Cluster Analysis of the Large-Scale Circulation

Abstract Forecasting the week 3/4 period presents many challenges, resulting in a need for improvements to forecast skill. At this distance from initial conditions, numerical models struggle to present skillful forecasts of temperature, precipitation, and associated extremes. One approach to address this is to utilize more predictable large-scale circulation regimes to make forecasts of temperature and precipitation anomalies, using the association between the regimes and surface weather obtained from reanalysis products. This study explores the utility of k-means cluster analysis on geopotential heights and their ability to make skillful regime predictions in the week 3/4 period. Using 14-day running means of European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) 500-hPa geopotential heights for the wintertime December–February (DJF) period, circulation regimes are identified using k-means clustering. Each period is assigned a cluster number, allowing the compositing of any reanalysis or observation variable to form cluster maps. Maps of 500-hPa height, 2-m temperature, precipitation, and storm-track anomalies are some of the variables composited. The utility of these relationships in a dynamical forecast setting is tested via Global Ensemble Forecast System v12 (GEFSv12) hindcasts and real-time ensemble suite forecasts. Week 3/4 deterministic and probabilistic experimental forecasts are then derived from cluster assignments using several methods. We find, via a conditional skill analysis, forecasts strongly correlated with a cluster exhibit greater skill for both dynamical model and cluster-derived forecasts. Our preliminary results represent a step forward to aid forecasters make more skillful assessments of the circulation regime and its associated surface weather for this challenging forecast time scale. Significance Statement Our paper links the local statistics of 14-day precipitation and temperature within the United States to very broad preferred patterns of winds and pressure over the wide Pacific–North American region. Such patterns, known as “circulation regimes,” provide the background that heavily influences local surface conditions. We find that forecasts for which midatmospheric anomalies are closely aligned with one or more circulation regimes are better at predicting the U.S. weather probabilities than other week 3–4 forecasts. A tool based on this finding identifies forecasts for which confidence is higher. Future work will be designed to further exploit the links between circulation regimes and weather to improve the accuracy of week 3–4 forecasts.

Sentinel‐1 Snow Depth Assimilation to Improve River Discharge Estimates in the Western European Alps

AbstractSeasonal snow is an important water source and contributor to river discharge in mountainous regions. Therefore the amount of snow and its distribution are necessary inputs for hydrological modeling. Recent research has shown the potential of the Sentinel‐1 radar satellite to map snow depth (SD) at sub‐kilometer resolution in mountainous regions. In this study we assimilate these new SD retrievals into the Noah‐Multiparameterization land surface model using an ensemble Kalman filter for the western European Alps. The land surface model was coupled to the Hydrological Modeling and Analysis Platform (HyMAP), a global flow routing scheme that provides simulations of routed river discharge. The performance with different precipitation forcing inputs, namely MERRA‐2 (with and without gauge based correction) and ERA5, was compared based on in situ precipitation and SD stations, with ERA5 leading to the best SD performance. The Sentinel‐1 based data assimilation (DA) results show small but systematic improvements for SD estimates, with the mean absolute error reducing from 36.4 cm for the open loop (OL) to 35.6 cm for the DA across all stations and timesteps, improving 318 out of 516 in situ sites. The DA updates in SD also result in enhanced snow water equivalent and discharge simulations. The median temporal correlation between discharge simulations and measurements increases from 0.73 to 0.78 for the DA. This study demonstrates the utility of the Sentinel‐1 SD retrievals to improve not only the representation of snow in mountain ranges, but also the snow melt contribution to river discharge, and hydrological modeling in general.

Coherent to semi-coherent transition of precipitates in Van der Merwe epitaxial films

Abstract The interplay of stresses of two coherent systems, specifically a coherent precipitate within an epitaxial film (in Van der Merve growth mode), gives rise to an interesting scenario; wherein the configurational effects and the strain energy landscape are enriched. This leads to an interdependent evolutionary pathway for the coherent to semi-coherent transition. The transition of the film-substrate interface to the semi-coherent state occurs beyond a critical thickness ( t c ) which arises from energy minimization. Using the model example of the precipitation of NbH0.5 precipitate in a Nb/Sapphire epitaxial system, the critical radii ( r film ∗ ) for model geometries have been computed. 3D finite element models have been used to compute the stress state and energetics of precipitates growing in an epitaxial film. The following important observations can be made based on the simulations. (1) The epitaxial stresses can lead to the stabilization of the coherent precipitate. (2) The transition to a semi-coherent film interface, characterized by an array of interfacial misfit dislocations, can energetically favor the semi-coherent state of the precipitate over the coherent state. The magnitude of r film ∗ depends on the spatial coordinates within the film. (3) The presence of Nb–H coherent precipitates can stabilize the coherent state of the film interface.

Multi-scale assessment of high-resolution reanalysis precipitation fields over Italy

This study focuses on the validation of high-resolution regional reanalyses to understand their effectiveness in reproducing precipitation patterns over Italy, a climate change hotspot characterized by coastal sea-land interaction and complex orography. Nine reanalysis products were evaluated, with the ECMWF global reanalysis ERA5 serving as a benchmark. These included both European (COSMO-REA6, CERRA) and Italy-specific (BOLAM, MERIDA, MERIDA-HRES, MOLOCH, SPHERA, VHR-REA_IT) datasets, using different models and parametrizations. The inter-comparison involved determining the effective resolution of daily precipitation fields using wavelet techniques and assessing intense precipitation statistics through frequency distributions. In-situ observations and observational gridded datasets were used to independently validate reanalysis precipitation fields. The capability of reanalyses to depict daily precipitation patterns was assessed, highlighting a maximum radius of precipitation misplacement of about 15 km, with notably lower skills during summer. An overall overestimation of precipitation was identified in the reanalysis climatological fields over the Po Valley and the Alps, whereas multiple products showed an underestimation of precipitations across the North-West coast, the Apennines, and Southern Italy. Finally, a comparison with a time-consistent observational dataset (UniMi/ISAC-CNR) revealed a non-stable deviation from observations in the annual precipitation cumulate of the reanalysis products analyzed. This should be taken into account when interpreting precipitation trends over Italy.

Examining Tropical Convection Features at Storm‐Resolving Scales Over the Maritime Continent Region

AbstractGlobal Storm Resolving Models (GSRMs) provide a way to understand weather and climate events across scales for better‐informed climate impacts. In this work, we apply the recently developed and validated CAM (Community Atmosphere Model)—MPAS (Model for Prediction Across Scales) modeling framework, based on the open‐source Community Earth System Model (CESM2), to examine the tropical convection features at the storm resolving scale over the Maritime Continent region at 3 km horizontal spacing. We target two global numerical experiments during the winter season of 2018 for comparison with observation in the region. We focus on the investigation of the representations of the convective systems, precipitation statistics, and tropical cyclone behaviors. We found that regional‐refined experiments show more accurate precipitation distributions, diurnal cycles, and better agreement with observations for tropical cyclone features in terms of intensity and strength statistics. We expect the exploration of this work will further advance the development and use of the storm‐resolving model in precipitation predictions across scales.

Mild and Subtle Synthesis of β-Ketoenamine COFs with High Crystallinity and Controllable Solubility Guided by a Monomer Preassembly Strategy.

The stability of covalent organic frameworks (COFs) is crucial for their applications in demanding environments. However, increasing the stability of COFs often comes with challenges such as higher synthesis difficulty, lower crystal quality, and reduced controllability during synthesis, making it difficult to regulate dimensions and morphology, thereby impacting the processing and shaping of stable COFs. Herein, the study presents a novel confined polymerization approach guided by hydrogen bonding preassembly to synthesize a soluble and stable COF featuring β-ketoenamine linkage. The presence of relatively weaker hydrogen bonds accelerates the orderly arrangement of monomers, ensuring appropriate spacing, and orientations among functional groups. This facilitates efficient covalent polymerization, leading to the creation of the framework while minimizing the "self-correction" mechanism during crystal growth, thereby enhancing the efficiency of COF synthesis. Furthermore, this method offers precise control over the size of the synthesized COF. The resulting crystalline COF can be toggled between dissolution and precipitation states, facilitating the fabrication of mixed matrix membranes (MMMs) through leveraging the solubility properties of COF. Overall, this pioneering strategy yields valuable insights for advancing weak bond assembly-mediated confined polymerization approaches, the controlled synthesis of stable COFs, and the preparation and processing of soluble COFs in diverse applications.

Assessment of CMIP6 models and multi-model averaging for temperature and precipitation over Iran

In this study, the performances of 40 Coupled Model Intercomparison Project Phase 6 are evaluated against observational data at synoptic stations in Iran using various evaluation criteria. The results reveal diverse model accuracy across different climate conditions and criteria, emphasizing particularly notable disparities in the nonstationarity R criterion compared to others. Although according to the ranking of the raw and bias-corrected outputs of CMIP6 GCMs for Iran, the NorESM2-MM, AWI-ESM-1-1-LR, and MPI-ESM1-2-LR models are consistently among the top six ranked models for precipitation in both raw and corrected outputs. For temperature, MPI-ESM1-2-LR, TaiESM1, INM-CM4-8, and IITM-ESM are consistently among the top six models for both the raw and bias-corrected outputs of CMIP6 GCMs. The Bias correction methods, including quantile mapping and linear scaling, integrated with Bayesian model averaging, were applied. While quantile mapping demonstrates superior performance and less disparity than linear scaling, it proves ineffective for correcting biases at stations with bias nonstationarity over time. The RMSE for monthly precipitation ranges from almost 0 to 200 mm, with a large RMSE value related to the high precipitation stations, and the monthly temperature exhibits a range of 0 to 4 °C. The use of a multi-model ensemble improves accuracy compared to individual models, resulting in a reduction in the differences between the minimum and maximum RMSE values from 178.6 to 91.0. Additionally, the range for mean absolute error decreases from 126.9 to 93.3, and the difference in the correlation coefficient narrows from 0.9 to 0.42. Averaging models after bias correction prevents significant fluctuations while maintaining higher accuracy, in contrast to the second method, which involves bias-correcting models after averaging.

Effects of Aqueous Solubility and Geochemistry on CO2 Storage in Offshore Basins

The increasing global focus on carbon capture and storage (CCS) has highlighted the potential for offshore CO2 sequestration, particularly following recent successes in onshore projects. This research investigates the qualitative analysis of carbon trapping efficiency in offshore basins, employing a GEM simulator to incorporate factors such as aqueous solubility and geochemistry. The findings reveal that anticlines represent ideal geological structures for carbon storage, effectively trapping a significant portion of injected CO2. For effective mineralization, it is crucial to dissolve CO2 into saline aquifers to generate H+, which facilitates the release of Ca2+ and Al3+ from anorthite. This process leads to the dissolution of anorthite and the precipitation of kaolinite, while calcite transitions from a dissolved state to a precipitated state over time. The analysis indicates that structural trapping provides the highest storage contribution during the injection phase, whereas residual gas trapping becomes dominant by the end of the simulation. Notably, it is observed that the storage contribution of structural trapping decreases from 28.39% to 19.05%, and the percentage increase in storage contributions of residual gas, solubility, ionic, and mineral trapping are 4.12%, 3.25%, 1.69%, and 0.28% for CO2 plus water injection, thereby improving the long-term security of CO2 storage in offshore basins. It is most beneficial to optimize the layout and design of the injection well to ensure a uniform distribution of carbon dioxide and to increase the injection rate.

Variations and Trends in 115 Years of Graded Daily Precipitation Records at Three Hydrometeorological Stations in Finland

This study investigated the variability and trends in 115 years (1909–2023) of daily precipitation observed at three hydrometeorological stations in southern (Kaisaniemi), central (Kajaani), and northern (Sodankylä) Finland. We also identified the most significant climate teleconnections influencing daily precipitation variability at these three stations during the period 1951–2023. The daily precipitation records were primarily classified into six grades, including very light (≤1 mm), light (1–≤5 mm), moderate (5–≤10 mm), heavy (10–≤15 mm), very heavy (15–≤20 mm), and extreme (>20 mm). On average, the most intense daily precipitation was determined at the Kaisaniemi station in southern Finland. At this station, however, very light and light precipitation showed the lowest frequency, but other graded daily precipitation events were the most frequent. At all three stations, the intensity of very light precipitation significantly declined during the past 115 years, while its frequency increased. The highest rates of such decreases and increases in the intensity and frequency of very light daily precipitation were found at the Sodankylä stations in northern Finland, respectively, but the lowest rates were at the Kaisaniemi station in the south. At the Kajaani station in central Finland, the intensity of light precipitation decreased, but very heavy precipitation intensified. At this station, however, the number of both moderate and heavy precipitation events increased over time. Finally, historical variations in both the intensity and frequency of graded daily precipitation events in Finland showed significant relationships with different climate teleconnections, particularly the Scandinavia (SCAND) and the North Atlantic Oscillation (NAO) patterns.

A simple and robust approach for adapting design storms to assess climate-induced changes in flash flood hazard

Hydrologists and civil engineers often use design storms to assess flash flood hazards in urban, rural, and mountainous catchments. These synthetic storms are not representations of real extreme rainfall events, but rather simplified versions parameterized to mimic extreme precipitation statistics often obtained from intensity–duration–frequency (IDF) curves. To construct design storms for the future climate, it is thus necessary first to recalculate IDF curves to represent rainfall under warmer conditions. We propose a framework for adjusting IDF curves and design storms to future climate conditions using the TENAX model, a novel statistical approach that can provide future short-duration precipitation return levels based on projected temperature changes. For most applications, information from climate models at the daily scale can be used to construct design storms at the sub-hourly scale without any downscaling or bias adjustment. Our approach is illustrated through a re-parameterization of the Chicago Design Storm (CDS) in the context of climate change. As a case study demonstration, we apply the TENAX model to data from the city of Zurich to calculate changes in the historical IDF curve for durations ranging from 10 min to 3 h. We then construct synthetic 100-year return period design storms based on the CDS for present and future climates and use the CAFlood model to produce flood inundation maps to assess changes in flood hazard. The codes for adapting design storms to climate change are simple to implement, easily applicable by practitioners, and made freely available.

Study of Intergranular Corrosion Behaviors of Mn-Increased 5083 Al Alloy with Controlled Precipitation States of Al6Mn Formed during Homogenization Annealing

In this study, as a vital part of the production of Mn-increased 5083 Al alloy, i.e., homogenization annealing before hot rolling, the target states of key Al6Mn precipitation, including the dispersed, initial coarsening and intensive coarsening states, were designed, and the corresponding precipitates formed via the control of the temperature and holding time in the annealing process. By means of metallographic corrosion and nitric acid mass loss tests (NAMLT) for assessing the intergranular corrosion (IGC) resistance, temperatures ranging from 175 °C to 225 °C were determined to induce a transition from sensitization to stabilization for this innovative 5083. At a temperature of 175 °C for a duration of up to 24 h (2 h, 4 h, 8 h, 16 h, 24 h), the results show that when the soak time is 24 h, the sample with initially coarsened Al6Mn phases has a lower degree of sensitization (DOS) compared to the samples with Al6Mn phases in both the dispersed and intensive coarsening states, and its NAMLT is reduced by 11% and 15%, respectively. Subsequently, transmission electron microscopy (TEM) analysis has investigated that for the sample with the best IGC resistance, i.e., that with initially coarsened Al6Mn phases, plate-like Al6Mn particles (200~500 nm) can act as heterogenous nucleation sites for β phases, driving their preferential precipitation on Al6Mn particles and resisting their precipitation along grain boundaries, ultimately improving the IGC resistance of 5083 Al alloy after homogenization annealing.

An Evaluation of Cloud‐Precipitation Structures in Mixed‐Phase Stratocumuli Over the Southern Ocean in Kilometer‐Scale ICON Simulations During CAPRICORN

AbstractA persistent shortwave radiative bias of Southern Ocean (SO) clouds in climate models is strongly associated with incorrect cloud phase representation, which impacts precipitation. Measurements characterizing precipitation in low‐level mixed‐phase clouds, which frequently form over the SO, are rare, and our understanding of precipitation efficacy within these clouds remains limited. The simulated surface precipitation bias has an indirect effect on determining global climate sensitivity and a direct impact on the hydrological cycle. This study investigates the representation of low clouds, cloud variability, and precipitation statistics over the SO in real‐case Icosahedral Nonhydrostatic (ICON) simulations at the kilometer scale. The simulations are contrasted with 48 hr of continuous shipborne observations of open and closed‐cell stratocumuli, south of Tasmania. Our simulations show the significance of heavily rimed particle formation, their in‐cloud growth, and subcloud melting to capture the observed cloud‐precipitation vertical structure. In addition, supercooled drizzle formation impacts the vertical structure and precipitation statistics. ICON captures the observed intermittency of precipitation even at a standard vertical resolution of 200 m in the boundary layer but only captures the observed sparse distribution of intense precipitation (>1 mm hr−1) when the maximum vertical resolution is reduced to 100 m. However, the simulations of the 2‐day accumulated precipitation and the radiative effect are largely insensitive to the vertical resolution. The cloud reflectivity of the broken cloud deck is underestimated due to negative biases in cloud optical depth.

Construction of black phosphorus/metal-organic framework composites for electrochemical detection of uric acid

The detection of uric acid (UA) concentration is of great significance for the prevention and treatment of related diseases. In this work, we used a static precipitation strategy to prepare a surface carbonized cobalt-based metal-organic frameworks (MOFs) composite black phosphorus nanosheets (BPNS) electrode material for electrochemical detection of UA. The results showed that the sensor had a linear detection range of UA concentration from 1 to 2000 μM in acetate buffer with pH=5.6. In addition, the sensor still maintains a peak current response of 91.9 % after one month, indicating its good stability and anti-interference performance. The enhancement of sensing performance is attributed to the fact that the composite material retains the excellent electronic transport properties of BPNS. At the same time, the composite of MOFs makes the specific surface area and porosity of the material increase, so that the stability of BPNS is significantly improved. This work will provide new ideas for the construction of stable and efficient UA electrochemical sensors.