Higher Skill Scores Research Articles

Developing a simple and efficient modeling solution for predicting key phenological stages of table grapes in a non-traditional viticulture zone in south Asia.

Phenological shifts are one of the most visible signs of climatic variability and change in the biosphere. However, modeling plant phenological responses has always been a key challenge due to climatic variability and plant adaptation. Grapevine is a phenologically sensitive crop and, thus, its developmental stages are affected by the increase in temperature. The goal of this study was to develop a temperature-based grapevine phenology model (GPM) for predicting key developmental stages for different table grape cultivars for a non-traditional viticulture zone in south Asia. Experiments were conducted in two vineyards at two locations (Chakwal and Islamabad) in the Pothawar region of Pakistan during the 2019 and 2020 growing seasons for four cultivars including Perlette, King's Ruby, Sugraone and NARC Black. Detailed phenological observations were obtained starting in January until harvest of the grapes. The Mitscherlich monomolecular equation was used to develop the phenology model for table grapes. There was a strong non-linear correlation between the Eichhorn and Lorenz phenological (ELP) scale and growing degree days (GDD) for all cultivars with coefficient of determinations (R2) ranging from 0.90 to 0.94. The results for model development indicated that GPM was able to predict phenological stages with high skill scores, i.e., a root mean square (RMSE) of 2.14 to 2.78 and mean absolute error (MAE) of 1.86 to 2.26 days. The prediction variability of the model for the onset timings of phenological stages was up to 3 days. The results also reveal that the phenology model based on GDD approach provides an efficient planning tool for viticulture industry in different grape growing regions. The proposed methodology, being a simpler one, can be easily applied to other regions and cultivars as a predictor for grapevine phenology.

Introduction to the NJIAS Himawari-8/9 Cloud Feature Dataset for climate and typhoon research

Abstract. The use of remote sensing methods to accurately measure cloud properties and their spatiotemporal changes has been widely welcomed in many fields of atmospheric research. The Nanjing Joint Institute for Atmospheric Sciences (NJIAS) Himawari-8/9 Cloud Feature Dataset (HCFD) provides a comprehensive description of cloud features over the East Asia and west North Pacific regions for the 7-year period from April 2016 to December 2022. Multiple cloud variables, such as cloud mask, phase/type, top height, optical thickness, and particle effective radius, as well as snow, dust, and haze masks, were generated from the visible and infrared measurements of the Advanced Himawari Imager (AHI) on board the Japanese geostationary satellites Himawari-8 and Himawari-9 using a series of recently developed cloud retrieval algorithms. Verifications with the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) 1 km cloud layer product and the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 cloud product (MYD06) demonstrate that the NJIAS HCFD gives higher skill scores than the Japanese Himawari-8/9 operational cloud product for all cloud variables except for cloud particle effective radius. The NJIAS HCFD even outperforms the MYD06 in nighttime cloud detection; cloud-top height, pressure, and temperature estimation; and infrared-only cloud-top phase determination. All evaluations are performed at the nominal 2 km scale, not including the effects of sub-pixel cloudiness or very thin cirrus. Two examples are presented to demonstrate applications of the NJIAS HCFD for climate and typhoon research. The NJIAS HCFD has been published in the Science Data Bank (https://doi.org/10.57760/sciencedb.09950, Zhuge 2023a; https://doi.org/10.57760/sciencedb.09953, Zhuge 2023b; https://doi.org/10.57760/sciencedb.09954, Zhuge 2023c; https://doi.org/10.57760/sciencedb.10158, Zhuge 2023d; https://doi.org/10.57760/sciencedb.09945, Zhuge 2023e).

Performance evaluation of lightning potential index and flash count using WRF microphysical parameters over Rajasthan and West Bengal, India

Accurate lightning prediction stands as a pressing global challenge, demanding robust solutions for safeguarding lives and valuable assets. In this study, we employ the mesoscale numerical model, Weather Research and Forecasting (WRF-ARW, version 4.0.3), to conduct numerical simulations of lightning occurrences during the 2021 monsoon season in Hooghly (on 07 June) and Jaipur (on 11 July). These events resulted in 31 and 11 casualties, respectively. The WRF model is integrated at horizontal resolutions of 9 km and 3 km for both regions, utilizing six-hourly NCEP-FNL datasets at a 0.25º resolution. The primary objective of this inquiry is to identify the most suitable microphysics scheme among WSM-6, NSSL-2, and MORRISON for a comprehensive assessment of lightning activity. Model performance is meticulously evaluated through skill scores, including Equitable Threat Score (ETS), False Alarm Rate (FAR), Accuracy (ACC), and Probability of Detection (POD), focusing specifically on hourly rainfall. Furthermore, a comprehensive spatial evaluation assesses the Lightning Potential Index and lightning flash count using the McCaul Method. The model-simulated results effectively depict lightning conditions in both regions, showing slight spatial and temporal discrepancies compared to observational datasets. Validation of the simulated lightning flash count is accomplished using data from the Lightning Detection Network (LDN) operated by the Indian Institute of Tropical Meteorology (IITM). The NSSL-2 microphysical scheme demonstrates noteworthy efficacy in identifying lightning occurrences in both regions. Rainfall representations correspond remarkably well with Indian Monsoon Data Assimilation and Analysis (IMDAA) data, indicating precipitation levels of 20–40 mm and 70–80 mm in Jaipur and Hooghly, respectively. The NSSL-2 microphysics scheme exhibits commendable proficiency, with consistently high model skill scores (ACC and POD ∼0.9) for lightning events. This study signifies a significant step toward the development of an operational lightning warning system, offering the potential to substantially reduce the risks associated with lightning occurrences. Consequently, such a system has the capacity to enhance safety and preparedness measures for regions affected by lightning phenomena.

A new method for predicting hurricane rapid intensification based on co-occurring environmental parameters

Tropical cyclones (TCs) that undergo Rapid Intensification (RI) can pose serious socioeconomic threats and can potentially result in major damaging impacts along coastal areas. Considering the complexity of various physical mechanisms that play a role in RI and its relatively low probability of occurrence, predicting RI remains a major operational challenge. In this study, we propose a simple deterministic binary classification model based on the co-occurrence of environmental parameters (MCE) to predict an RI event. More specifically, the model determines the possibility of RI based on a simple count of the number of environmental predictors deemed favorable and unfavorable. We compare our model results to logistic regression (LR) and decision tree (DT) models, well-trained using the same set of environmental predictors. Results reveal that at an RI threshold of 30 kt, the MCE exhibits a critical success index score of 0.233 which is 14% higher than DT and LR model performances. When tested at multiple RI thresholds, the MCE displays relatively higher skill scores across multiple metrics. By simultaneously evaluating the favorability of predictors, the MCE is able to comparatively reduce the number of false alarms predicted when certain predictors are unfavorable toward RI. Interpreting these model results to gain a physical understanding of how co-occurring environmental parameters can affect RI, we highlight future directions for using models based on the MCE approach to understand and predict TC RI as well as other meteorological extremes.

The impact of a summative national prescribing assessment and curriculum type on the development of the prescribing competence of junior doctors

PurposeThe primary aim of this study was to investigate the effect of including the Dutch National Pharmacotherapy Assessment (DNPA) in the medical curriculum on the level and development of prescribing knowledge and skills of junior doctors. The secondary aim was to evaluate the relationship between the curriculum type and the prescribing competence of junior doctors.MethodsWe re-analysed the data of a longitudinal study conducted in 2016 involving recently graduated junior doctors from 11 medical schools across the Netherlands and Belgium. Participants completed three assessments during the first year after graduation (around graduation (+ / − 4 weeks), and 6 months, and 1 year after graduation), each of which contained 35 multiple choice questions (MCQs) assessing knowledge and three clinical case scenarios assessing skills. Only one medical school used the DNPA in its medical curriculum; the other medical schools used conventional means to assess prescribing knowledge and skills. Five medical schools were classified as providing solely theoretical clinical pharmacology and therapeutics (CPT) education; the others provided both theoretical and practical CPT education (mixed curriculum).ResultsOf the 1584 invited junior doctors, 556 (35.1%) participated, 326 (58.6%) completed the MCQs and 325 (58.5%) the clinical case scenarios in all three assessments. Junior doctors whose medical curriculum included the DNPA had higher knowledge scores than other junior doctors (76.7% [SD 12.5] vs. 67.8% [SD 12.6], 81.8% [SD 11.1] vs. 76.1% [SD 11.1], 77.0% [12.1] vs. 70.6% [SD 14.0], p < 0.05 for all three assessments, respectively). There was no difference in skills scores at the moment of graduation (p = 0.110), but after 6 and 12 months junior doctors whose medical curriculum included the DNPA had higher skills scores (both p < 0.001). Junior doctors educated with a mixed curriculum had significantly higher scores for both knowledge and skills than did junior doctors educated with a theoretical curriculum (p < 0.05 in all assessments).ConclusionOur findings suggest that the inclusion of the knowledge focused DNPA in the medical curriculum improves the prescribing knowledge, but not the skills, of junior doctors at the moment of graduation. However, after 6 and 12 months, both the knowledge and skills were higher in the junior doctors whose medical curriculum included the DNPA. A curriculum that provides both theoretical and practical education seems to improve both prescribing knowledge and skills relative to a solely theoretical curriculum.

Precipitation nowcasting using ground radar data and simpler yet better video prediction deep learning

ABSTRACT Skillful quantitative precipitation nowcasting (QPN) is important for predicting precipitation in the upcoming few hours and thus avoiding significant socioeconomic damage. Recent QPN studies have actively adopted deep learning (DL) to generate precipitation maps using sequences of ground radar data. Although high skill scores in forecasting precipitation areas of weak intensity (~1 mm/h) have been achieved, the horizontal movement of precipitation areas could not be accurately simulated, exhibiting poor forecasting skills for stronger intensities. For lead times up to 120 min, this study suggests using an improved radar-based QPN model that utilizes a state-of-the-art DL model termed simpler yet better video prediction (SimVP). An independent evaluation using ground radar data in South Korea from June to September 2022 demonstrated that the proposed model outperformed the existing DL models in terms of critical score index (CSI) with a lead time of 120 min (0.46, 0.23, and 0.09 for 1, 5, and 10 mm/h thresholds, respectively). Three case analyses were conducted to reflect various precipitation conditions: heavy rainfall, typhoons, and fast-moving narrow convection events. The proposed SimVP-based QPN model yielded robust performance for all cases, producing a comparable or highest CSI at the lead time of 120 min with a 1 mm/h threshold (0.49, 0.69, and 0.29 for heavy rainfall, typhoon, and narrow convection, respectively). Qualitative evaluation of the model indicated better results in terms of displacement movement and reduced underestimation than other models under the high variability of precipitation patterns of the three cases. A comparison of model complexity among DL-QPN models was conducted, taking into consideration operational applications across various study areas and environments. The proposed approach is expected to provide a new baseline for DL-based QPN, and the improved prediction using the proposed model can lead to reduced socioeconomic damage incurred as a result of short-term intense precipitation.

The lactation skill gaps of multidisciplinary paediatric healthcare professionals in the United Kingdom.

Breastfeeding is an important public health priority and may be particularly beneficial for medically complex infants and children. However, childhood illness and disability are associated with increased challenges and lower breastfeeding rates. The Baby Friendly Initiative has been shown to increase initiation of breastfeeding and improve health professional skills although as yet the standards have not been adopted in paediatrics. Previous studies have found breastfeeding knowledge gaps among paediatric nurses, and a recent systematic review highlighted insufficient lactation support, discouragement by healthcare professionals and lack of resources. The aim of this survey of UK paediatric professionals was to establish their self-defined confidence and skills supporting breastfeeding. An online survey was developed to explore associations between level of training and staff confidence and perceived skill, to establish whether there is evidence that more training and/or higher breastfeeding training credentials improve skill. In total, 409 professionals, including paediatric doctors at all grades, paediatric nurses and allied health professionals, were included in the analysis. This study identified specific skill gaps among professionals. Many healthcare professionals felt that different skills and specific training are required to support medically complex children. Several professionals noted that existing breastfeeding training focuses on establishing breastfeeding in healthy newborns rather than sick children in paediatrics. Participants were asked about 13 clinical competencies, and an aggregate skill score was calculated. Multiple univariate analysis of variance found that more extensive training and higher credentials are correlated with higher skill scores (p ≤ 0.001), whereas type of professional was not. Despite this being a relatively motivated sample of healthcare professionals, the findings of this study suggest that breastfeeding skills are patchy and inconsistent, and particularly lacking when it comes to more complex clinical scenarios. This is significant, because it may mean that children who have more significant illness or medical complexity are disproportionately affected by gaps in knowledge and skill. Medically complex children encounter many barriers to optimal feeding - including absence of designated paediatric lactation staff, resources and support - and may have challenges such as low tone, higher calorie need and transitioning to the breast after ventilation or enteral feeding. Current skill gaps indicate that existing training would be insufficient, and bespoke paediatric breastfeeding training based on identified clinical challenges is thus justified.

Simulations of drifting fish aggregating device (dFAD) trajectories in the Atlantic and Indian Oceans

Tropical tuna purse-seine fisheries deploy thousands of human-made drifting fish aggregating devices (dFADs) annually, raising a number of concerns regarding ecosystem impacts. In this study, we explored the use of a Lagrangian particle-tracking model to simulate the drift of dFADs in the Atlantic and Indian Oceans. We simulated more than 100,000 dFADs trajectories using the Lagrangian tool Ichthyop forced with velocity fields from an ocean model output (GLORYS12V1) and two satellite-derived ocean currents products (OSCAR and GEKCO). Importantly, through a collaborative agreement with the French frozen tuna producers’ organization we had access to the true locations of all dFADs along their drift and could therefore evaluate the accuracy of our simulations. The accuracy was assessed by comparing the observed and simulated trajectories in terms of spatial distribution, separation distance, and a non-dimensional skill score (an index based on separation distances normalized by net displacements of dFADs). In the two oceans, simulations forced with GLORYS12V1 were more accurate than with OSCAR and GEKCO, probably due to the differences in the spatio-temporal resolution of the forcing products. When we compared multiple depths for GLORYS12V1, the model performed better at 0 m in the Indian Ocean and at 5 m in the Atlantic Ocean, which could be related to the longer vertical structure of dFADs in the Atlantic Ocean. We showed that including a windage factor did not improve the accuracy of modeled dFADs trajectories. We found that mean model-data separation distances were similar in both oceans, exceeding 100 km after 6–8 days of drift. While separation distances between simulated and observed trajectories show that model errors were similar in the two oceans, the generally longer distances traveled by dFADs in the Indian Ocean than in the Atlantic Ocean lead to considerably higher skill scores in the former than in the latter. This explains the relatively good predictive ability of the model to represent mean dFAD densities at the basin scale in both oceans, while at the same time indicates higher prediction skills for the movements of individual dFADs in the Indian Ocean than in the Atlantic Ocean.

Seasonal Prediction of Tropical Cyclones and Storms over the Southwestern Indian Ocean Region Using the Generalized Linear Models

Tropical cyclones (TCs) and storms (TSs) are among the devastating events in the world and southwestern Indian Ocean (SWIO) in particular. The seasonal forecasting TCs and TSs for December to March (DJFM) and November to May (NM) over SWIO were conducted. Dynamic parameters including vertical wind shear, mean zonal steering wind and vorticity at 850 mb were derived from NOAA (NCEP-NCAR) reanalysis 1 wind fields. Thermodynamic parameters including monthly and daily mean Sea Surface Temperature (SST), Outgoing Longwave Radiation (OLR) and equatorial Standard Oscillation Index (SOI) were used. Three types of Poison regression models (i.e. dynamic, thermodynamic and combined models) were developed and validated using the Leave One Out Cross Validation (LOOCV). Moreover, 2 × 2 square matrix contingency tables for model verification were used. The results revealed that, the observed and cross validated DJFM and NM TCs and TSs strongly correlated with each other (p ≤ 0.02) for all model types, with correlations (r) ranging from 0.62 - 0.86 for TCs and 0.52 - 0.87 for TSs, indicating great association between these variables. Assessment of the model skill for all model types of DJFM and NM TCs and TSs frequency revealed high skill scores ranging from 38% - 70% for TCs and 26% - 72% for TSs frequency, respectively. Moreover, results indicated that the dynamic and combined models had higher skill scores than the thermodynamic models. The DJFM and NM selected predictors explained the TCs and TSs variability by the range of 0.45 - 0.65 and 0.37 - 0.66, respectively. However, verification analysis revealed that all models were adequate for predicting the seasonal TCs and TSs, with high bias values ranging from 0.85 - 0.94. Conclusively, the study calls for more studies in TCs and TSs frequency and strengths for enhancing the performance of the March to May (MAM) and December to October (OND) seasonal rainfalls in the East African (EA) and Tanzania in particular.

Downscaling WGHM-Based Groundwater Storage Using Random Forest Method: A Regional Study over Qazvin Plain, Iran

Climate change, urbanization, and a growing population have led to a rapid increase in groundwater (GW) use. As a result, monitoring groundwater changes is essential for water managers and decision-makers. Due to the lack of reliable and insufficient in situ information, remote sensing and hydrological models may be counted as alternative sources to assess GW storage changes on regional and global scales. However, often, these hydrological models have a low spatial resolution for water-related applications on a small scale. Therefore, the main purpose of this study is to downscale the GW storage anomaly (GWSA) of the WaterGAP Global Hydrology Model (WGHM) from a coarse (0.5 degrees) to a finer spatial resolution (0.1 degrees) using fine spatial resolution auxiliary datasets (0.1 degrees), such as evaporation (E), surface (SRO), subsurface runoff (SSRO), snow depth (SD), and volumetric soil water (SWVL), from the ERA5-Land model, as well as the global precipitation (Pre) measurement (GPM-IMERG) product. The Qazvin Plain in central Iran was selected as the case study region, as it faces a severe decline in GW resources. Different statistical regression models were tested for the GWSA downscaling to find the most suitable method. Moreover, since different water budget components (such as precipitation or storage) are known to have temporal lead or lag relative to each other, the approach also incorporates a time shift factor. The most suitable regression model with the highest skill score during the training-validation was selected and applied to predict the final 0.1-degree GWSA. The downscaled results showed high agreement with the in situ groundwater levels over the Qazvin Plain on both interannual and monthly time scales, with a correlation coefficient of 0.989 and 0.62, respectively. Moreover, the downscaled product represents clear proof that the developed downscaling technique is able to learn from high-resolution auxiliary data to capture GWSA features at a higher spatial resolution. The major benefit of the proposed method lies in the utilization of only the auxiliary data that are available with global coverage and are free of charge, while not requiring in situ GW records for training or prediction. Therefore, the proposed downscaling technique can potentially be applied at a global scale and to aquifers in other geographical regions.

Generation and Assessment of ARGO Sea Surface Temperature Climatology for the Indian Ocean Region

ARGO program was conceived with an aim to generate near real-time ocean observations as the primary in-situ sources for use in operational oceanography studies. Two decades-long ARGO near-surface temperature data set was used for generating monthly gridded ARGO sea surface temperature (ASST) product on a climatological scale. Data interpolating variational analysis (DIVA) method was employed for generating the product with a spatial resolution of 0.25° x 0.25° for the Tropical Indian Ocean. This monthly ASST product was evaluated using five different climatological SST products derived from in-situ and satellite measurements. Various statistics such as BIAS, RMSE, coefficient of correlation, and skill scores were generated to evaluate the reliability of the ASST product. Further, the ASST product was validated with climatological in-situ SST obtained from RAMA and OMNI moorings deployed in the Indian Ocean. Statistical comparisons showed low BIAS and RMSE, and high correlation and skill scores with most of the buoys locations and the gridded SST products. Results concluded that the near-surface temperature data from ARGO can be used along with other SST data sets in the generation of high-resolution blended SST products.

Relative Importance of Large‐Scale Environmental Variables to the World‐Wide Variability of Thunderstorms

AbstractThis study uses a 16‐yr Tropical Rainfall Measuring Mission (TRMM) Convective Features (CFs) and ERA‐Interim reanalysis data to investigate the relative importance of four large‐scale environmental variables to thunderstorms with random forest models. These four variables include Convective Available Potential Energy (CAPE), Convective Inhibition (CIN), low‐level wind shear, and warm cloud depth (WCD). First, these selected four environmental variables show a distinguished difference between CFs with and without lightning flashes. Specifically, CFs with at least one flash have higher CAPE, CIN, and lower WCD than those without lightning. Then, using these four variables, the geographical distribution of thunderstorms, especially the land‐ocean contrast in the occurrence of thunderstorms, is closely reproduced with a global random forest model. Such results suggest that a random forest model with key large‐scale environmental variables can be a useful tool to estimate the occurrence of global lightning thunderstorms. The study also investigates the relative importance of the selected variables to the occurrence of thunderstorms regionally. Relatively higher skill scores in the regional random forest model than the global one indicate the variation of roles of large scale environment variables over different regions. Though the data‐driven models can be utilized to estimate the occurrence of global thunderstorms, how to link the regional relative importance of these variable to the physical processes of thunderstorms needs further investigation.

Classification of Precipitation Types Based on Machine Learning Using Dual-Polarization Radar Measurements and Thermodynamic Fields

An accurate classification of the precipitation type is important for forecasters, particularly in the winter season. We explored the capability of three supervised machine learning (ML) methods (decision tree, random forest, and support vector machine) to determine ground precipitation types (no precipitation, rain, mixed, and snow) for winter precipitation. We provided information on the particle characteristics within a radar sampling volume and the environmental condition to the ML model with the simultaneous use of polarimetric radar variables and thermodynamic variables. The ML algorithms were optimized using predictor selection and hyperparameter tuning in order to maximize the computational efficiency and accuracy. The random forest (RF) had the highest skill scores in all precipitation types and outperformed the operational scheme. The spatial distribution of the precipitation type from the RF model showed a good agreement with the surface observation. As a result, RF is recommended for the real-time precipitation type classification due to its easy implementation, computational efficiency, and satisfactory accuracy. In addition to the validation, this study confirmed the strong dependence of precipitation type on wet-bulb temperature and a 1000–850 hPa layer thickness. The results also suggested that the base heights of the radar echo are useful in discriminating non-precipitating area.

Evaluation and Usefulness of Lightning Forecasts Made with Lightning Parameterization Schemes Coupled with the WRF Model

Abstract The evaluation and usefulness of lightning prediction for the Indian subcontinent are demonstrated. Implementation of the lightning parameterizations based on storm parameters, in the Weather Research and Forecasting (WRF) Model, with different microphysics schemes are carried out. With the availability of observed lightning measurements over Maharashtra from the lightning detection network (LDN), lightning cases have been identified during the pre-monsoon season of 2016–18. Lightning parameterization based on cloud top height defined by a reflectivity threshold factor of 20 dBZ is chosen. Initial analysis is carried out for 16 lightning events with four microphysical schemes for the usefulness in lightning prediction. Objective analysis is carried out and quantitative model performance (skill scores) is assessed based on observed data. The skills are evaluated for 10- and 50-km2 boxes from the 1-km domain. There is good POD of 0.86, 0.82, 0.85, and 0.84, and false alarm ratio (FAR) of 0.28, 0.25, 0.29, and 0.26 from WSM6, Thompson, Morrison, and WDM6, respectively. There is an overestimation in lightning flash with a spatial and temporal shift. The fractional skill score is evaluated as a function of spatial scale with neighborhoods from 25 to 250 km. These high skill scores and high degree of correlation between observations and model simulation gives us confidence to use the system for real-time operational forecast over India. The skill for 2019 and 2020 pre-monsoon are calculated to address the predictability of operational lightning prediction over India. Significance Statement A high-resolution model, namely, the Weather Research and Forecasting (WRF) Model, with multiple microphysics parameterization schemes and lightning parameterization is used here. The objective analysis is carried out for the lightning cases over India and the quantitative performance is assessed. The results highlight that there is fairly good probability of detection (POD) of 0.86, 0.82, 0.85, and 0.84 and false alarm ratio (FAR) of 0.28, 0.25, 0.29, and 0.26 from four different microphysical schemes (WSM6, Thompson, Morrison, and WDM6, respectively). These high skill scores and high degree of correlation between observations and model simulation gives us confidence to use the system for real-time operational forecast. The validation of lightning forecast system deployed over India for five pre-monsoon months in real time is carried out, which gives POD of 0.90, FAR of 0.64, hit rate of 0.57, and POFD of 0.50 for the whole Indian region.

Evaluation of Seven Gap-Filling Techniques for Daily Station-Based Rainfall Datasets in South Ethiopia

Meteorological stations, mainly located in developing countries, have gigantic missing values in the climate dataset (rainfall and temperature). Ignoring the missing values from analyses has been used as a technique to manage it. However, it leads to partial and biased results in data analyses. Instead, filling the data gaps using the reference datasets is a better and widely used approach. Thus, this study was initiated to evaluate the seven gap-filling techniques in daily rainfall datasets in five meteorological stations of Wolaita Zone and the surroundings in South Ethiopia. The considered gap-filling techniques in this study were simple arithmetic means (SAM), normal ratio method (NRM), correlation coefficient weighing (CCW), inverse distance weighting (IDW), multiple linear regression (MLR), empirical quantile mapping (EQM), and empirical quantile mapping plus (EQM+). The techniques were preferred because of their computational simplicity and appreciable accuracies. Their performance was evaluated against mean absolute error (MAE), root mean square error (RMSE), skill scores (SS), and Pearson’s correlation coefficients (R). The results indicated that MLR outperformed other techniques in all of the five meteorological stations. It showed the lowest RMSE and the highest SS and R in all stations. Four techniques (SAM, NRM, CCW, and IDW) showed similar performance and were second-ranked in all of the stations with little exceptions in time series. EQM+ improved (not substantial) the performance levels of gap-filling techniques in some stations. In general, MLR is suggested to fill in the missing values of the daily rainfall time series. However, the second-ranked techniques could also be used depending on the required time series (period) of each station. The techniques have better performance in stations located in higher altitudes. The authors expect a substantial contribution of this paper to the achievement of sustainable development goal thirteen (climate action) through the provision of gap-filling techniques with better accuracy.

Quantile forecast of renewable energy generation based on Indicator Gradient Descent and deep residual BiLSTM

Accurate generation forecasting can effectively accelerate the use of renewable energy in hybrid energy systems, contributing significantly to the delivery of the net-zero emission target. Recently, neural-network-based quantile forecast models have shown superior performance on renewable energy generation forecasting, partially because they have subtly embedded quantile forecast evaluation metrics into their loss functions. However, the non-differentiability of involved metrics has rendered their metric-embedded loss functions not everywhere-derivable, resulting in inapplicability of gradient-based training approaches. Instead, they have resorted to heuristic searches for Neural Network (NN) training, bringing low training efficiency and a rigid restriction on the size of the resultant NN. In this paper, the Indicator Gradient Descent (IGD) is proposed to overcome the non-differentiability of involved metrics, and several metric-embedded loss functions are innovatively customized combining IGD, enabling NNs to be trained efficiently in a ‘gradient-descent-like’ manner. Moreover, the deep Bidirectional Long Short-Term Memory (BiLSTM) is adopted to capture the periodicity of renewable generation (diurnal and seasonal patterns), and the residual technique is used to improve the training efficiency of the deep BiLSTM. Finally, a Deep Quantile Forecast Network (DQFN) based on IGD and deep residual BiLSTM is developed for wind and solar power quantile forecasting. Practical experiments in four cases have verified the effectiveness and efficiency of DQFN and IGD, where DQFN has achieved the lowest average proportion deviations (all below 1.7%) and the highest skill scores.

The Effect of Hots-Oriented Worksheets on Heat and Kinetic Theory of Gases on Students' Critical and Creative Thinking Ability with Guided Inquiry Model in Grade XI SMA Al-Istiqamah Pasaman Barat

This research aims to see how effect of HOTS-oriented worksheets on heat and kinetic theory of gases on students' critical and creative thinking ability with guided inquiry model in grade XI SMA Al-Istiqamah Pasaman Barat. The type of research used is quasi-experimental. In this research, two classes were used as the sample class, where class XI IPA 1 was the experimental class and class XI IPA 2 was the control class. The difference in treatment between the two classes is that in the experimental class the product given is HOTS-oriented worksheets while in the control class the product given is conventional student worksheets in schools. For the treatment equation in the two classes, the learning model used is the guided inquiry learning model. The technique for collecting data was a written test with HOTS oriented questions. Critical and creative thinking skills are the basis for assessment in this research. The results of the study after being given treatment in both classes found that the highest skill score between the two classes was the value obtained by the experimental class. Based on the research that has been done, it is concluded that after being given treatment to the sample class, there is an increase in students' critical and creative thinking skills for each indicator. The improvement of students' critical and creative thinking skills on each indicator got higher results in the experimental class than the control class.

Advanced Dual-Satellite Method for Detection of Low Stratus and Fog near Japan at Dawn from FY-4A and Himawari-8

The detection of low stratus and fog (LSF) at dawn remains limited because of their optical features and weak solar radiation. LSF could be better identified by simultaneous observations of two geostationary satellites from different viewing angles. The present study developed an advanced dual-satellite method (DSM) using FY-4A and Himawari-8 for LSF detection at dawn in terms of probability indices. Optimal thresholds for identifying the LSF from the spectral tests in DSM were determined by the comparison with ground observations of fog and clear sky in/around Japan between April to November of 2018. Then the validation of these thresholds was carried out for the same months of 2019. The DSM essentially used two traditional single-satellite tests for daytime such as the 0.65-μm reflectance (R0.65), and the brightness temperature difference between 3.7 μm and 11 μm (BTD3.7-11); in addition to four more tests such as Himawari-8 R0.65 and BTD13.5-8.5, the dual-satellite stereoscopic difference in BTD3.7-11 (ΔBTD3.7-11), and that in the Normalized Difference Snow Index (ΔNDSI). The four were found to show very high skill scores (POD: 0.82 ± 0.04; FAR, 0.10 ± 0.04). The radiative transfer simulation supported optical characteristics of LSF in observations. The LSF probability indices (average POD: 0.83, FAR: 0.10) were constructed by a statistical combination of the four to derive the five-class probability values of LSF occurrence in a grid. The indices provided more details and useful results in LSF spatial distribution, compared to the single satellite observations (i.e., R0.65 and/or BTD3.7-11) of either LSF or no LSF. The present DSM could apply for remote sensing of environmental phenomena if the stereoscopic viewing angle between two satellites is appropriate.

Telemedicine Awareness, Knowledge, Attitude, and Skills of Health Care Workers in a Low-Resource Country During the COVID-19 Pandemic: Cross-sectional Study.

BackgroundSince the onset of the COVID-19 pandemic, several health care programs intended to provide telemedicine services have been introduced in Libya. Many physicians have used these services to provide care and advice to their patients remotely.ObjectiveThis study aimed to provide an overview of physicians’ awareness, knowledge, attitude, and skill in using telehealth services in Libya.MethodsIn this cross-sectional study, we administered a web-based survey to health care workers in Libya in May 2020. The questionnaire collected information on physicians’ general demographic characteristics, ability to use a computer, and telemedicine awareness, knowledge, attitude, and skills.ResultsAmong 673 health care workers who responded to the survey, 377 (56%) and 248 (36.8%) reported high awareness and high computer skill scores, respectively, for telemedicine. Furthermore, 582 (86.5%) and 566 (82.6%) health care workers reported high knowledge and high attitude scores, respectively. We observed no significant differences in awareness, knowledge, attitude, and skill scores among physicians employed at public, private, or both types of hospitals. We observed significant differences in the mean awareness (P<.001), attitude (P=.001), and computer skill scores (P<.001) , where the score distribution of the groups based on the ability to use computers was not similar. Knowledge scores did not significantly differ among the three groups (P=.37). Respondents with professional computer skills had significantly higher awareness (χ23=14.5; P<.001) and attitude (χ23=13.5; P=.001) scores than those without professional computer skills. We observed significant differences in the mean computer skill scores of the groups (χ23=199.6; P<.001).ConclusionsThe consequences of the COVID-19 pandemic are expected to persist for a long time. Hence, policy programs such as telemedicine services, which aim to address the obstacles to medical treatment owing to physical distancing measures, will likely continue for a long time. Therefore, there is a need to train and support health care workers and initiate government programs that provide adequate and supportive health care services to patients in transitional countries.

Evaluation of subseasonal impacts of the MJO/BSISO in the East Asian extended summer

The Madden–Julian Oscillation (MJO)/Boreal Summer Intraseasonal Oscillation (BSISO) has been considered an important climate mode of variability on subseasonal timescales for East Asian summer. However, it is unclear how well the MJO/BSISO indices would serve as guidance for subseasonal forecasts. Using a probabilistic forecast model determined through multiple linear regression (MLR) with MJO, ENSO, and long-term trend as predictors, we examine lagged impacts of each predictor on East Asia extended summer (May–October) climate from 1982 to 2015. The forecast skills of surface air temperature (T2m) contributed by each predictor is evaluated for lead times out to five weeks. We also provide a systematic evaluation of three commonly used, real-time MJO/BSISO indices in the context of lagged temperature impacts over East Asia. It is found that the influence of the trend provides substantial summertime skill over broad regions of East Asia on subseasonal timescales. In contrast, the MJO influence shows regional as well as phase dependence outside the tropical band of the main action centers of the MJO convective anomalies. All three MJO/BSISO indices generate forecasts that yield high skill scores for week 1 forecasts. For some initial phases of the MJO/BSISO, skill reemerges over some regions for lead times of 3–5 weeks. This emergence indicates the existence of windows of opportunity for skillful subseasonal forecasts over East Asia in summer. We also explore the dynamics that contribute to the elevated skills at long lead times over Tibet and Taiwan–Philippine regions following the initial state of phases 7 and 5, respectively. The elevated skill is rooted in a wave train forced by the MJO convective heating over the Arabian Sea and feedbacks between MJO convection and SSTs in Taiwan–Philippine region. Two out of the three commonly used MJO/BSISO indices tend to identify MJO events that evolve consistently in time, allowing them to serve as reliable predictors for subseasonal forecasts for up to 5 weeks.