Critical Index Research Articles

Thermal Conductivity and Thermal Diffusivity of Iron in the Temperature Range of 300–1700 K

Abstract—Using the laser flash method, the thermal diffusivity (a) of carbonyl iron is measured in the temperature range of 300–1700 K with a detailed study of the critical region 980–1170 K. The initial experimental data in the field of magnetic phase transformation are processed by the scaling power law. The values of the critical indexes for thermal diffusivity (\(\gamma {\kern 1pt} ',\) γ) are obtained below and above the Curie temperature TС = 1048 ± 5 K: \(\gamma {\kern 1pt} '\) = 0.51 and γ = 0.35, which in absolute value significantly exceed the value of the characteristic critical index for the heat capacity (γ ≈ –0.1). The thermal conductivity (λ) is calculated from measured data of the thermal diffusivity. The results are compared with the known literature data, special attention is paid to the behavior of the curves a(T), λ(T) in the region of the magnetic phase transformation. A table of recommended temperature dependences for a and λ along with estimated errors has been developed.

Validation of chirps gauge – satellite based rainfall dataset over Nicaragua, 2011 - 2021

Rainfall is a key input for many weather and climate numerical models. Therefore the strong need to have a dense enough monitoring network for this parameter. Satellite-based rainfall products have emerged in recent decades as an alternative to the more expensive gauge stations. However, a proper validation of such satellite-based products against gauge data must be performed before using their data. This study presents a validation of CHIRPS dataset against gauge data for 17 stations across Nicaragua. The performance of the product was validated at different temporal scales (daily, pentadal, monthly and annual) by different error metrics. A total of six quantitative error metrics was assessed: Bias Percentage (PBIAS), Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Pearson’s r and Nash Stucliffe Efficiency (NSE). A total three categorical indices were assessed at daily time scale: Probability of Detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI). The results showed that CHIRPS dataset have better performance at monthly and annual time scales, while it is not capable of adequately represent the daily variability.

Modelling polarization effect in electrical conductivity of frozen soil

Unfrozen water content is a critical index for understanding the hydromechanical behavior of frozen soil in cold regions. The electrical conductivity of frozen soil has been commonly used to determine the unfrozen water content in soil in both laboratory and field tests. However, the polarization effect caused by the frequency of the alternating current significantly influences measurements of the electrical conductivity of frozen soil. The mechanism of the polarization effect has yet to be thoroughly investigated and understood, and a precise theoretical model is lacking. This study first develops a polarization model for soil at positive and negative temperatures based on the multi-peak Cole-Cole model, which can theoretically give a better frequency range for electrical conductivity. Furthermore, the relationship between the test frequency range and temperature is established based on Arrhenius' law. A series of electrical conductivity tests are performed on multiple soil samples at positive and negative temperatures to validate the proposed model. It found that the polarization area of silt and sand increased rapidly with increasing frequency and gradually expanded to low frequencies with decreasing temperature. This study finally recommends an accurate reference range of test frequency for measuring the electrical conductivity of frozen soils.

Development of the management for parenteral nutrition traceability in a standard hospital

Objectiveto develop the traceability control and the hazard analysis in the processes of parenteral nutrients (PN). Methoda standardized graphical notation was generated, describing in detail each of the stages in the overall process. The presence of hazards was analysed by sequencing decisions. The existence of Control Points (CP) or Critical Control Points (CCP) was estimated by Criticality Index (CI) for each hazard taking into account the probability of occurrence and the severity of the damage. The threshold for the IC was set in 6. Resultsa specific flow chart for the management and tra-ceability of PN was obtained, defining each of the stages in CPs (validation and transcription of the prescription and administration) or CCPs (preparation, storage and infusion pump -flow and filter-). Stages regarding the delivery, the recovery and the recycle of the packing material of PNs are not considered CPs and, therefore, they were not included in the dashboard. ConclusionsPN must be dealt with in the frame of a standardized management system in order to improve patient safety, clinical relevance, maximize resource efficiency and minimize procedural issues. The proposed system provides a global management model whose steps are fully defined, allowing monitoring and verification of PN. It would be convenient to make use of a software application to support the monitoring of the traceability management and to store the historical records in order to evaluate the system.

Self-weight and thermal bifurcation analysis of functionally graded vertical annular plates

The present paper thoroughly investigates the influence of structural weight on the thermal buckling of standing circular and annular plates. The structure is composed of symmetrically functionally graded materials (FGM) in terms of its thickness. The first-order theory of shear deformation along with nonlinear strain–displacement equations has been employed to derive the governing equations of the FG plate. First, the asymmetric pre-buckling path of the structure under the influence of weight and heat is calculated by a semi-analytical method. The plate's stability relations are extracted using the adjacent equilibrium criterion and virtual work principle. Then, the stability equations are analyzed by the 2D generalized differential quadrature (GDQ) method. Parametric results are presented based on the analysis of the FGM power-law index, geometric ratios, and edge constraints on the buckling of the vertical plate. The study reveals the critical power-law index and thickness at which the structure becomes unstable, shedding light on the previously neglected calculation of weight-induced buckling in vertical structures. Also, the effect of weight on the buckling mode shapes of heavy plates and disturbing its symmetry is demonstrated. Additionally, this study presents the first investigation into the effect of weight on the thermal stability of annular FG plates.

Multi-Criteria Evaluation Model of Management for Weaned Piglets and Its Relations with Farm Performance and Veterinary Medicine Consumption.

Weaned piglets, being immature, demand careful handling to mitigate post-weaning stress in order to avoid immunosuppression and the use of antimicrobials to palliate the effects of disease outbreaks due to poor management. The objective of this work is to design a quick scan calculator or multi-criteria evaluation model of management for weaned piglets, founded on 10 critical indices covering post-weaning management aspects based on hygienic measures and management of facilities and animals. These include pre-weaning handling, batch management, biosecurity, water management, feed management, health program, stockmen training, temperature, ventilation, and floor conditions and density to relate handling and hygiene practices with farm performance and the consumption of veterinary medication. Each index carries a maximum score of ten, with evaluations derived from different management factors that make up each index (from three to eight factors were evaluated per index). Their cumulative score reflects the degree of adequacy of on-farm management. Therefore, a perfectly managed farm would achieve 100 points. The calculator underwent testing on 23 intensive farms with a total population of close to 16,000 sows and more than 400,000 weaned piglets, revealing the highest mean scores in floor conditions and density (8.03 out of 10) and pre-weaning handling and health programs (6.87 and 6.28, respectively). Conversely, the lowest scores corresponded to temperature, ventilation, water management, and stockmen training (4.08, 4.32, 4.81, and 4.93, respectively). The assessed farms averaged a global score of 56.12 out of 100 (from 37.65 to 76.76). The calculator's global score correlated with key post-weaning productivity and piglet health indicators, such as the feed conversion ratio, mortality rate, and piglet production cost, with r values of -0.442, -0.437, and -0.435, respectively (p < 0.05). Additionally, it negatively correlated with medication costs per piglet (r = -0.414; p < 0.05) and positively with annual farm productivity (r = 0.592; p < 0.01). To enhance management, hygiene, and prevention, farms should prioritize addressing indices with the lowest scores, thereby reducing medication consumption and enhancing productivity and health outcomes. Additionally, this quick scan calculator can be used for benchmarking purposes.

A novel hybrid grey system forecasting model based on seasonal fluctuation characteristics for electricity consumption in primary industry

As a critical economic index, the annual electricity consumption plays an essential role in reflecting the domestic economic environment. For the sustainable development of the electricity power industry, an accurate electricity consumption forecasting model can not only guide the power generation work in advance but also help to determine the regulation law of electricity economic development. To accurately describe the seasonal fluctuation characteristics of electricity consumption in China's primary industry, Fourier modified grey forecasting model based on seasonal fluctuation characteristics and initial condition optimization (FDSGM(1,1,x(β),γ)) was put forward. Based on the empirical data of seasonal electricity consumption in China's primary industry, comparing with SGM(1,1), RSGM(1,1) and DSGM(1,1), the minimum mean absolute percentage errors (MAPEs) in the training set was reduced to 0.2 %, while that in the testing set was also reduced to 6.02 %, which was significantly better than those of the previous three forecasting models and was also markedly better than the prediction accuracy of reference [15]. The results indicated that FDSGM(1,1,x(β),γ) could identify seasonal fluctuations and variations and was a reliable seasonal forecasting tool that could be used to ensure sustainable development of electricity markets.

Enhancing daily rainfall prediction in urban areas: a comparative study of hybrid artificial intelligence models with optimization algorithms

Forecasting precipitation is a crucial input to hydrological models and hydrological event management. Accurate forecasts minimize the impact of extreme events on communities and infrastructure by providing timely and reliable information. In this study, six artificial intelligent hybrid models are developed to predict daily rainfall in urban areas by combining the firefly optimization algorithm (FA), invasive weed optimization algorithm (IWO), genetic particle swarm optimization algorithm (GAPSO), neural network (ANN), group method of data handling (GMDH), and wavelet transformation. Optimization algorithms increase forecasting accuracy by controlling all stages. A variety of criteria are used for validating the models, including correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), critical success index (CSI), probability of detection (POD), and false alarm ratio (FAR). The proposed models are also evaluated in an urban area in Ahvaz, Iran. The GAPSO-Wavelet-ANN model is superior to other models for predicting daily rainfall, with an RMSE of 1.42 mm and an R of 0.9715.

Evaluation of global precipitation products for meteorological drought assessment with respect to IMD station datasets over India

In-situ precipitation data is the most reliable data for the drought assessment. However, gridded datasets are commonly used due to limited availability of station data. However, the gridded data may exhibit bias compared to station data. The present study has been undertaken to examine the performance of gridded datasets from global precipitation products (gauge-interpolated, merged from various sources, re-analysis and satellite products) for the assessment of meteorological drought with reference to station datasets of the India Meteorological Department (IMD) from 2000 to 2019 at six drought zones over India. Various statistical metrics were used to check the performance of the precipitation products at four seasons of India. The Standardised Precipitation Index (SPI) was used at a 1-month timescale to evaluate the meteorological drought characteristics. The gridded precipitation products, in regions of high rainfall, tended to underestimate precipitation during the monsoon season and overestimate it during the non-monsoon season. When precipitation was analysed spatially using statistical parameters, the product Integrated Multi-Satellite Retrievals (IMERG Final-Run) outperformed all the other precipitation products in the four seasons and six drought zones over India. For drought characteristics evaluation, the products Climate Prediction Center (CPC) Morphing Technique (CMORPH) and Global Precipitation Climatology Centre (GPCC) outperformed all the other products in the six drought zones over India. Further, in order to rank the precipitation products, the Spearman rank correlation coefficient (SCC) and critical success index (CSI) were used. The analysis results showed that IMERG Final-Run, GPCC and Multi-Source Weighted-Ensemble Precipitation (MSWEP-PastNoGauge) ranked best out of all the precipitation products when analysed for drought monitoring and assessment at six drought zones over India. Overall, IMERG Final-Run outperformed all the other precipitation products for drought monitoring, while TerraClimate performed poorly in most of the drought zones and in India as a whole. The study's findings will help choose suitable gridded precipitation dataset for meteorological drought assessment across six homogeneous drought zones and in India as a whole, aiding operational drought risk management and early warnings.

Dam concrete quality evaluation and prediction model based on fractal characteristics of elastic-wave computed tomography

The evaluation and prediction of dam concrete quality have important theoretical significance for preventing catastrophes in concrete dams and predicting their long-term safety characteristics. To realize a comprehensive index for the evaluation and prediction of dam concrete characteristics, the fractal characteristics of dam concrete were analyzed using elastic-wave computed tomography (EWCT). The dam concrete quality evaluation model was established by combining the defined EWCT fractal dimensions with the strength and maturity of the dam concrete. Furthermore, a dam concrete artificial neural network (ANN) prediction model with a multi-index input layer was developed. The results indicated that the EWCT images exhibited the early evolution and distribution characteristics of dam concrete specimens, and there was a strong positive correlation between the EWCT fractal dimension and the age of the dam concrete. The grey relational analysis coefficient between the EWCT fractal dimension and fully graded concrete strength was similar to the coefficient between the wet-screened and fully graded concretes. Among the five dam concrete detection indices used in the ANN, the weight contribution rate of the EWCT fractal dimension to the dam concrete ANN prediction model was 23.15 %. In conclusion, the fractal dimension may be used as a critical index in dam concrete detection to pave the way for developing intelligent dam concrete detection and monitoring based on non-destructive technology.

Application and evaluation of CNN-LSTM classification regression based multi-source precipitation data fusion model in water resources research in Qinghai Province

Qinghai Province is located in the Qinghai-Tibet Plateau region, with complex and diverse topography and sparse precipitation stations, which makes it difficult to obtain reliable precipitation data. This study proposes a classification and regression model based on a deep learning algorithm, which combines a convolutional neural network (CNN) and a long short-term memory neural network (LSTM), with the CNN extracting the spatial features of multi-source data, the LSTM capturing their temporal dependencies. The regression results are used to determine whether rainfall is occurring and to further calibrate the non-rainfall component of the precipitation forecast results. ERA5, IMERG, CHIRPS and DEM were selected as feature data and rain gauge data as label data. The findings indicate that the proposed CNN-LSTM classification regression model (CLCR) is superior to other models (CNN, CNN-LSTM, LSTM). The Kling-Gupta efficiency (KGE) of the data fused using CLCR was 0.66, which was significantly better than that of the raw rainfall data (0.53, -0.36, 0.34) and other models (0.58, 0.65, 0.63). CLCR also showed more performance in daily precipitation detection than other models and raw precipitation data, with Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR) 0.61, 0.25 and 0.76 respectively. This study generated a high-precision daily rainfall dataset with a precision of 0.01° resolution for 2013-2017 in Qinghai Province, which provides reliable data support for hydrological studies in Qinghai Province.

Automated Lightning Jump (LJ) Detection from Geostationary Satellite Data

Abstract Rapid increases in the flash rate (FR) of a thunderstorm, so-called lightning jumps (LJs), have potential for nowcasting applications and to increase lead times for severe weather warnings. To date, there are some automated LJ algorithms that were developed and tuned for ground-based lightning locating systems. This study addresses the optimization of an automated LJ algorithm for the Geostationary Lightning Mapper (GLM) lightning observations from space. The widely used σ-LJ algorithm is used in its original form and in an adapted calculation including the footprint area of the storm cell (FRarea LJ algorithm). In addition, a new relative increase level (RIL) LJ algorithm is introduced. All algorithms are tested in different configurations, and detected LJs are verified against National Centers for Environmental Information severe weather reports. Overall, the FRarea algorithm with an activation FR threshold of 15 flashes per minute and a σ-level threshold of 1.0–1.5 as well as the RIL algorithm with FR threshold of 15 flashes per minute and RIL threshold of 1.1 are recommended. These algorithms scored the best critical success index (CSI) of ∼0.5, with a probability of detection of 0.6–0.7 and a false alarm ratio of ∼0.4. For daytime warm-season thunderstorms, the CSI can exceed 0.5, reaching 0.67 for storms observed during three consecutive days in April 2021. The CSI is generally lower at night and in winter.

Applications of colorimetric sensors for non-destructive predicting total volatile basic nitrogen (TVB-N) content of Fujian oyster (Crassostrea angulata)

Total volatile basic nitrogen (TVB-N) content is one of the critical safety evaluation indexes in seafood. This study investigated the colorimetric sensing array (CSA) technique with two data collection blocks, imaging and visible near-infrared spectroscopy, for non-destructive prediction of TVB-N content in Fujian oysters. Three data fusion strategies coupled with variable selection approaches including variable combination population analysis (VCPA), ant colony optimization, and competitive adaptive reweighted sampling were applied to establish the partial least squares (PLS) model for predicting the TVB-N content of oysters. Better prediction performance was obtained using data fusion of two information blocks than using individual block data. The results showed that the high-level-VCPA strategy showed the best performance with the correlation coefficient of the prediction set (Rp) at 0.9128. This work demonstrates that the use of CSA has the potential for non-destructive monitoring of TVB-N content in Fujian oysters.

FOG/VISIBILITY FORECAST AND VERIFICATION AT IGI AIRPORT, NEW DELHI DURING THE WINTER SEASONS OF 2020-21 &amp; 2021-22

The reduction in visibility due to fog leads to cancellation of flights, delays and diversions at IGI Airport, New Delhi during the winter season. Accurate prediction of fog/ visibility is required within sufficient lead time to make the flight operations safer, economical and more convenient. This study attempts to improve the fog/visibility forecast at IGI airport for the months of December and January during the winter seasons of 2020-21 and 2021-22. Various meteorological parameters required to predict the fog/visibility are obtained from IMD GFS forecasted meteorological information which are valid for next 24 and 48-hours. Forecast verification was performed using the METAR and synoptic observations. The results for 2020-21 fog/visibility forecast for 24-hours are promising with a POD of 0.92, critical success index as 0.68, and false alarm ratio as 0.28. Moreover, the 2021-22 results for 24 -hours fog /visibility forecast are also observed to be promising with a POD of 0.73, critical success index of 0.54 and false alarm ratio at 0.32. This method gives fairly accurate predictions in point locations and can also be used for a larger spatial area. However, the 48-hours forecast performance needs further improvement. The method also predicted the wind speed and relative humidity which were found to be in agreement with the observed data to a great extent. In the upcoming years, the fog/visibility forecast method will be more robust with better prediction accuracy of meteorological parameters from model outputs and observations.&#x0D;

Assessment of Deep Learning-Based Nowcasting Using Weather Radar in South Korea

This study examines the effectiveness of various deep learning algorithms in nowcasting using weather radar data from South Korea. Herein, the algorithms examined include RainNet, ConvLSTM2D U-Net, a U-Net-based recursive model, and a generative adversarial network. Moreover, this study used S-band radar data from the Ministry of Environment to assess the predictive performance of these models. Results show the efficacy of these algorithms in short-term rainfall prediction. Specifically, for a threshold of 0.1 mm/h, the recursive RainNet model achieved a critical success index (CSI) of 0.826, an F1 score of 0.781, and a mean absolute error (MAE) of 0.378. However, for a higher threshold of 5 mm/h, the model achieved an average CSI of 0.498, an F1 score of 0.577, and a MAE of 0.307. Furthermore, some models exhibited spatial smoothing issues with increasing rainfall-prediction times. The findings of this research hold promise for applications of societal importance, especially for preventing disasters due to extreme weather events.

A Fuzzy Synthetic Evaluation Analysis of the Drivers of Urban Expansion on Peri-Urban Lands in Ilorin, Nigeria

Aim: To examine the drivers of urban expansion on peri-urban areas in Ilorin, Kwara State, Nigeria.&#x0D; Study Design: Cross-sectional survey research.&#x0D; Place and Duration of Study: Eyenkorin, Kwara State 2022.&#x0D; Methodology: From the literature review, fifteen (15) drivers of urban expansion at the peri-urban areas were highlighted. Afterwards a pilot survey was done on the 15 factors among experts in academia with relevant experiences in the built environment. The 15 factors were affirmed adequate for the study. A structured questionnaire survey on a 4-Point Likert Scale was administered to 332 household heads in the study area to colate opinions on how the highlighted factors influence urban expansion on the peripheral land. Weighted Mean Score, Factor Analysis and Fuzzy Synthetic Evaluation were used to analyze the factors driving urban expansion in the study area. Weighted Mean Score was used to evaluate the individual factors driving urban expansion of peripheral lands in the study area. The Factor Analysis helped to identify the principal individual factors driving urban expansion at the study area and how they are related with to one another. Fuzzy Synthetic matrix was used to reveal the categories of factors driving urban expansion at the study area in order of their importance.&#x0D; Results: From study, Occupation and living related factors with a criticality index of 3.12 are the most critical drivers of urban expansion towards the periphery in the study area. This is followed by Accessibility comfort and Land speculation factors with a criticality index of 2.96; and Religious and Institutional factors with a criticality index of 2.72. However, in the study area, Socio-Cultural related factors with a criticality index of 1.22 are not significant drivers of urban expansion towards the periphery in the study area.&#x0D; Conclusion: The study revealed that there has been exponential spatial urban land use expansion in the peripheral area considered for the study. The study revealed that the factors driving urban expansion to the peripheral area centered around job/occupation related factors, housing and living factors, accessibility factors, religious and institutional factors. For the purpose of environmental preservation and a structured land use, attention should be given to the pace and pattern of urban expansion to achieve a sustainable land use. Hence, there is need to start giving attention to how urban expansion occurs and the land use activities that clusters around the landmarks driving the urban expansion at the peri-urban areas in order to ensure sustainable urban expansion especially in developing countries where this can be an oversight. This will help to optimize a balanced urban land use dynamics for the present and the future preservation of the environment.

Luminescence properties of ZnSxO1-x:Ce3+ phosphors with tunable short fluorescence lifetime

Fluorescence lifetime of phosphors is a critical index in the field of high energy physics and astrophysical detection. A series of ZnSxO1-x:0.05Ce3+ phosphors with tunable short fluorescence lifetime were prepared by performing high temperature solid state reaction method. The phosphors exhibited two mixed phases consisting of the hexagonal phase ZnO and the hexagonal phase ZnS. They are spherical and the average particle size is 2.24 μm. As the component content of the ZnS in ZnSxO1-x:0.05Ce3+ phosphors varies, the emission wavelength can be tuned from 448 nm to 495 nm, the short fluorescence lifetime can be tuned within the range of 6 μs–200 μs. By performing exponential fitting, we obtained the equation for the variation of fluorescence lifetime of ZnSxO1-x:0.05Ce3+ phosphors with ZnS fraction.

Improving daily precipitation estimations in a high mountainous watershed by developing a new downscaling method with spatially varying coefficients

Accurate estimates of spatial patterns of daily precipitation are critically important for numerous hydrologic applications. However, the estimation of daily precipitation is still challenging, especially in regions with sparse observation networks and complex terrain. Although satellite precipitation products can offer continuous precipitation fields on a daily scale, the data contain large bias and coarse spatial resolution, which limit the application of these products in local studies. Here, we developed a new spatially varying coefficient downscaling approach to yield a daily precipitation field with high-resolution and high-accuracy in a high mountainous watershed. First, the random effect eigenvector spatial filtering (RESF) model was introduced and improved by considering the nonlinear term; second, the residuals were corrected by applying the simple inverse distance weighted (IDW) method. Using the proposed method, named RESF-IDW, the Global Satellite Mapping of Precipitation (GSMaP) was downscaled from 0.1° to 0.01° by integrating reanalyzed atmospheric data and environmental variables as auxiliary information. The performance of RESF-IDW was evaluated and compared with the geographically weighted regression (GWR) method and the original GSMaP precipitation products at 20 national and automatic stations by using leave-one-out cross validation. The results show that compared to the original GSMaP products, RESF-IDW, RESF, GWR-IDW, and GWR exhibited improvements in their mean values of correlation coefficient (CC) by 24.5%, 17.9%, 12.0%, and 3.2%, respectively. Additionally, their mean values of mean absolute error (MAE) improved by 31.2%, 14.9%, 18.7%, and 2.4%, respectively, while their mean values of root mean square error (RMSE) improved by 24.1%, 18.5%, 9.6%, and 3.8%, respectively. The RESF-IDW model can be used to correctly improve the original GSMaP product under different precipitation intensities, with critical success index (CSI) improvements of 38.81%, 43.14% and 44.58% for low, medium and high precipitation intensities, respectively. The use of residual correction enhances the spatial accuracy (MAE decreases by 19.43% on average) and the precipitation detection capability (CSI increases by 12.09% on average) of the RESF estimations. Based on the error metrics, the RESF-IDW model can be used to generate improved precipitation estimates on a daily scale and has a good capability for precipitation detection over different intensities. The proposed method in this study provides a promising way to generate accurate daily precipitation fields with high spatial resolution over data sparse and high mountain regions.

Sentinel-1-based analysis of the severe flood over Pakistan 2022

Abstract. In August and September 2022, Pakistan was hit by a severe flood, and millions of people were impacted. The Sentinel-1-based flood mapping algorithm developed by Technische Universität Wien (TU Wien) for the Copernicus Emergency Management Service (CEMS) global flood monitoring (GFM) component was used to document the propagation of the flood from 10 August to 23 September 2022. The results were evaluated using the flood maps from the CEMS rapid mapping component. Overall, the algorithm performs reasonably well with a critical success index of up to 80 %, while the detected differences can be primarily attributed to the time difference of the algorithm's results and the corresponding reference. Over the 6-week time span, an area of 30 492 km2 was observed to be flooded at least once, and the maximum extent was found to be present on 30 August. The study demonstrates the ability of the TU Wien flood mapping algorithm to fully automatically produce large-scale results and how key data of an event can be derived from these results.

Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning

Abstract. Quantitative precipitation nowcasting (QPN) can help to reduce the enormous socioeconomic damage caused by extreme weather. The QPN has been a challenging topic due to rapid atmospheric variability. Recent QPN studies have proposed data-driven models using deep learning (DL) and ground weather radar. Previous studies have primarily focused on developing DL models, but other factors for DL-QPN have not been thoroughly investigated. This study examined four critical factors in DL-QPN, focusing on their impact on forecasting performance. These factors are the deep learning model (U-Net, as well as a convolutional long short-term memory, or ConvLSTM), input past sequence length (1, 2, or 3 h), loss function (mean squared error, MSE, or balanced MSE, BMSE), and ensemble aggregation. A total of 24 schemes were designed to measure the effects of each factor using weather radar data from South Korea with a maximum lead time of 2 h. A long-term evaluation was conducted for the summers of 2020–2022 from an operational perspective, and a heavy rainfall event was analyzed to examine an extreme case. In both evaluations, U-Net outperformed ConvLSTM in overall accuracy metrics. For the critical success index (CSI), MSE loss yielded better results for both models in the weak intensity range (≤ 5 mm h−1), whereas BMSE loss was more effective for heavier precipitation. There was a small trend where a longer input time (3 h) gave better results in terms of MSE and BMSE, but this effect was less significant than other factors. The ensemble by averaging results of using MSE and BMSE losses provided balanced performance across all aspects, suggesting a potential strategy to improve skill scores when implemented with optimal weights for each member. All DL-QPN schemes exhibited problems with underestimation and overestimation when trained by MSE and BMSE losses, respectively. All DL models produced blurry results as the lead time increased, while the non-DL model retained detail in prediction. With a comprehensive comparison of these crucial factors, this study offers a modeling strategy for future DL-QPN work using weather radar data.