Background: Environmental factors such as meteorological conditions and air pollutants are recognized as important for human health, where mortality and morbidity of certain diseases may be related to abrupt climate change or air pollutant concentration. In the literature, environmental factors have been identified as risk factors for chronic diseases such as ischemic heart disease. However, the likelihood evaluation of the disease occurrence probability due to environmental factors is missing. Method: We defined people aged 51–90 years who were free from ischemic heart disease (ICD9: 410–414) in 1996–2002 as the susceptible group. A Bayesian conditional logistic regression model based on a case-crossover design was utilized to construct a risk information system and applied to data from three databases in Taiwan: air quality variables from the Environmental Protection Administration (EPA), meteorological parameters from the Central Weather Bureau (CWB), and subject information from the National Health Insurance Research Database (NHIRD). Results: People living in different geographic regions in Taiwan were found to have different risk factors; thus, disease risk alert intervals varied in the three regions. Conclusions: Disease risk alert intervals can be a reference for weather bureaus to issue health warnings. With early warnings, susceptible groups can take measures to avoid exacerbation of disease when meteorological conditions and air pollution become hazardous to their health.

The tropical cyclone (TC) intensity forecast from the Weighted Analog Intensity Prediction (WAIP) was evaluated using 63 Philippine TC cases from 2014 to 2017 to determine its applicability as baseline intensity forecast guidance of the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA).The method generates a rank-weighted average of intensity evolutions of 10 historical analogs from the 1945 to 2014 Joint Typhoon Warning Center best tracks that closely resemble the PAGASA official forecast track and initial intensity at the time the forecast is generated.WAIP proved to be more skillful in providing intensity forecast at 12 to 96 h and less skillful at 120 h relative to persistence.Verification revealed that WAIP had significantly smaller mean absolute error and consistently smaller intensity biases up to 96 h.However, the small sample size at 96 h due to the limitations in the extent of the observed track and reference track forecast from PAGASA suggests that the result may not fully represent the model performance within the Philippine Area of Responsibility at 96 h.The probability distribution of intensities at 36, 72, and 96 h predicted by the model showed that the statistical model may not fully capture the full range of the observed intensities or the extreme values, with the model struggling to predict lower range of intensity values with increasing forecast intervals.Three TC cases are presented to emphasize the model dependence on the accuracy of the reference track forecast and the number and representativeness of available historical analogs for a particular forecast scenario.

In preparation for the Fourth International Workshop on Tropical Cyclone Landfall Processes (IWTCLP-IV), a summary of recent research studies and the forecasting challenges of tropical cyclone (TC) rainfall has been prepared. The extreme rainfall accumulations in Hurricane Harvey (2017) near Houston, Texas and Typhoon Damrey (2017) in southern Vietnam are examples of the TC rainfall forecasting challenges. Some progress is being made in understanding the internal rainfall dynamics via case studies. Environmental effects such as vertical wind shear and terrain-induced rainfall have been studied, as well as the rainfall relationships with TC intensity and structure. Numerical model predictions of TC-related rainfall have been improved via data assimilation, microphysics representation, improved resolution, and ensemble quantitative precipitation forecast techniques. Some attempts have been made to improve the verification techniques as well. A basic forecast challenge for TC-related rainfall is monitoring the existing rainfall distribution via satellite or coastal radars, or from over-land rain gauges. Forecasters also need assistance in understanding how seemingly similar landfall locations relative to the TC experience different rainfall distributions. In addition, forecasters must cope with anomalous TC activity and landfall distributions in response to various environmental effects.

An automated technique has been developed for the detection and tracking of tropical cyclone like vortices (TCLVs) in numerical weather prediction models, and especially for ensemble-based models. A TCLV is detected in the model grid when selected dynamic and thermodynamic fields meet specified criteria. A backward-and-forward extension from the mature stage of the track is utilized to complete the track. In addition, a fuzzy logic approach is utilized to calculate the TCLV fuzzy combined-likelihood value (TFCV) for representing the TCLV characteristics in the ensemble forecast outputs. The primary objective of the TCLV tracking and TFCV maps is for use as an evaluation tool for the operational forecasters. It is demonstrated that this algorithm efficiently extracts western North Pacific TCLV information from the vast amount of ensemble data from the NCEP Global Ensemble Forecast System (GEFS). The predictability of typhoon formation and activity during June December 2008 is also evaluated. The TCLV track numbers and TFCV averages around the formation locations during the 0-96-h period are more skillful than for the 102-384-h forecasts. Compared to weak tropical cyclones (TCs; maximum intensity <= 50 kt), the storms that eventually become stronger TCs do have larger TFCVs. Depending on the specified domain size and the ensemble track numbers to define a forecast event, some skill is indicated in predicting the named TC activity. Although this evaluation with the 2008 typhoon season indicates some potential, an evaluation with a larger sample is necessary to statistically verify the reliability of the GEFS forecasts.

* Corresponding author address: Cheng-Shang Lee, Department of Atmospheric Sciences, National Taiwan University, 1, Sec 4, Roosevelt Road, Taipei 106, Taiwan ROC; email: cslee@nat.as.ntu.edu.tw