Traditional radar-based rainfall estimation is typically done by known functional relationships between the rainfall intensity (R) and radar measurables, such as R–Z_h, R–(Z_h, Z_DR), etc. One of the biggest advantages of machine learning algorithms is the applicability to a non-linear relationship between a dependent variable and independent variables without any predefined relationships. We explored the potential use of two supervised machine learning methods (regression tree and random forest) in rainfall estimation using dual-polarization radar variables. The regression tree does not require normalization and scaling of data; however, this method is quite unstable since each split depends on the parent split. Since the random forest is an ensemble method of regression trees, it has less variability in prediction compared with regression trees, but consumes more computer resources. We considered several different configurations for machine learning algorithms with different sets of dependent and independent variables. The random forest model was appropriately tuned. In the test of variable importance, the specific differential phase (differential reflectivity) was the most important variable to predict the rainfall rate (residual that is the difference between the true rainfall rate and the one estimated from the R–Z relationship). The models were evaluated by 10-fold cross-validation. The best model was the random forest model using a residual with the non-classified training set. The results indicated that the machine learning algorithms outperformed the traditional R–Z relationship. Then, we applied the best machine learning model to an S-band dual-polarization radar (Mt. Myeonbong) and validated the result with ground rain gauges. The results of the application to radar data showed that the estimates of the residuals had spatial variability. The stratiform and weak rain areas had positive residuals while convective areas had negative residuals, indicating that the spatial error structure driven by the R–Z relationship was well captured by the model. The rainfall rates of all pixels over the study area were adjusted with the estimated residuals. The rainfall rates adjusted by residual showed excellent agreement with the rain gauge, especially at high rainfall rates.

Pediatric lipid management recommendations have evolved from selective screening to universal screening to identify and target therapy for genetic dyslipidemias. Data on the success of the selective screening guidelines for lipid testing, dyslipidemia detection, and lipid management are conflicting. To determine temporal trends in lipid testing, dyslipidemia categories and pharmacotherapy in a cohort of 653,642 individual youth aged 2 to 20 years from 2002 to 2012. Summary data on lipid test results, lipid-lowering medicine (LLM) dispensings, and International Classification of Diseases, Ninth Revision diagnoses were compiled from the virtual data warehouses of 5 sites in the Cardiovascular Research Network. Temporal trends were determined using linear regression. Among the average 255,160 ± 25,506 children enrolled each year, lipid testing declined from 16% in 2002 to 11% in 2012 (P < .001 for trend). Among the entire population, the proportion newly detected each year with low-density lipoprotein cholesterol >190 mg/dL, a value commonly used to define familial hypercholesterolemia, increased over time from 0.03% to 0.06% (P = .03 for trend). There was no significant change over time in the proportion of the yearly population initiated on LLM or statins specifically (0.045 ± 0.009%, P = .59 [LLM] and 0.028 ± 0.006%, P = .25 [statin]). Although lipid testing declined during 2002 to 2012, the detection of familial hypercholesterolemia-level low-density lipoprotein cholesterol increased. Despite this increased detection, pharmacotherapy did not increase over time. These findings highlight the need to enhance lipid screening and management strategies in high-risk youth.

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.

This study introduces methods for generating fused precipitation data by applying radial basis function (RBF) interpolation, which integrates radar reflectivity-based data with ground-based precipitation gauge measurements. Rain gauges provide direct point rainfall measurements near the ground, while radars capture the spatial variability of precipitation. However, radar-based estimates, particularly for extreme rainfall events, often lack accuracy due to their indirect derivation from radar reflectivity. The study aims to produce high-resolution gridded ground precipitation data by merging radar rainfall estimates with the precise rain gauge measurements. Rain gauge data were sourced from automated synoptic observing systems (ASOSs) and automatic weather systems (AWSs), while radar data, based on hybrid surface rainfall (HSR) composites, were all provided by the Korea Meteorological Administration (KMA). Although RBF interpolation is a well-established technique, its application to the merging of radar and rain gauge data is unprecedented. To validate the accuracy of the proposed method, it was compared with traditional approaches, including the mean field bias (MFB) adjustment method, and kriging-based methods such as regression kriging (RK) and kriging with external drift (KED). Leave-one-out cross-validation (LOOCV) was performed to assess errors by analyzing overall error statistics, spatial errors, and errors in rainfall intensity data. The results showed that the RBF-based method outperformed the others in terms of accuracy.

Accurately generating high-resolution surface grid datasets often involves merging multiple weather observation networks and addressing the challenge of network heterogeneity. This study aims to tackle the problem of accurately interpolating temperature data in regions with a complex topography. To achieve this, we introduce a deterministic interpolation method that incorporates elevation to enhance the accuracy of temperature datasets. This method is particularly valuable for areas with intricate terrains. Our robust methodology integrates a network harmonization method with radial basis function (RBF) interpolation for complex topographical regions. The method was tested on 10 min average temperature data from Jeju Island, South Korea, over 2 years that had a spatial resolution of 100 m. The results show a significant reduction of 5.5% in error rates, from an average of 0.73 °C to 0.69 °C, by incorporating all adjusted data. Integrating a parameterized nonlinear temperature profile further enhances accuracy, yielding an average reduction of 4.4% in error compared to the linear model. The spatial interpolation method, based on regression-based radial basis functions, demonstrates a 6.7% improvement over regression-based kriging for the same temperature profile. This research offers a valuable approach for precise temperature interpolation, especially in regions with a complex topography.

The COVID-19 pandemic that began at the end of 2019 has caused hundreds of millions of infections and millions of deaths worldwide. COVID-19 posed a threat to human health and profoundly impacted the global economy and people’s lifestyles. The United States is one of the countries severely affected by the disease. Evidence shows that the spread of COVID-19 was significantly underestimated in the early stages, which prevented governments from adopting effective interventions promptly to curb the spread of the disease. This paper adopts a Bayesian hierarchical model to study the under-reporting of COVID-19 at the state level in the United States as of the end of April 2020. The model examines the effects of different covariates on the under-reporting and accurate incidence rates and considers spatial dependency. In addition to under-reporting (false negatives), we also explore the impact of over-reporting (false positives). Adjusting for misclassification requires adding additional parameters that are not directly identified by the observed data. Informative priors are required. We discuss prior elicitation and include R functions that convert expert information into the appropriate prior distribution.