ABSTRACT:The direct positional errors (DPEs) in tropical cyclone (TC) track predictions over the Philippine domain of the Weather Research Forecasting (WRF) model's Advance Research WRF (ARW) was studied. The 3 year dataset (i.e. 2012-2014) was categorized (i.e. tropical storm (TS), severe tropical storm (STS), typhoon (TY)) and the marginal distributions of the DPEs over the short-range (i.e. 84 h) prognosis of the model were analysed. The decreasing magnitudes and spread of DPEs at increasing TC intensity were determined. The median DPEs for the model initialization were 70 km (TS), 40 km (STS), 30 km (TY) which progressively increased by 50-90% (all subsets), 130-220% (TS and TY), 430% (TY) for the 1, 2 and 3 day forecasts, respectively. The variations in timing, frequency and length of significant DPE increments for successive lead time levels, as well as the daily significant DPE increments from the model initialization on the TC subsets were also studied. The formulations of the forecast cone of uncertainty (CONU) were likewise incorporated.

Tropical cyclones (TCs) can have large impacts over land and marginal seas in the western North Pacific (WNP), especially in the Southeast Asia (SEA) region where countries, such as the Philippines and Vietnam, are among the most exposed in the world to TC-related hazards. Each year there are over 20 TCs on average affecting SEA, with about 50% of them occurring in the peak-season of July-September (JAS), where the TC season is usually defined as June-November. Accurate prediction of peak-season SEA TCs up to several months ahead can provide an early warning service, which may significantly reduce the damage from TCs to economic activity and social welfare.Dynamical general circulation models (GCMs) have been developed as a useful tool for seasonal TC prediction, especially in recent years with the development of high-resolution climate models related to the rapidly increased computing capability. Dynamical seasonal forecast systems have been developed in the major meteorological

A regionally focused index of Boreal summer intraseasonal variability (RISV) modes for southeast Asia is used to study their impact on regional rainfall extremes. Four phases to characterise the northward/northeastward propagation of active and suppressed convective regions are defined, which show a clear and distinct impact on the mean and extreme precipitation over various sub-regions of Southeast Asia. Northward propagation of convection is strongly coupled to wind convergence at lower atmospheric levels. A threshold-based precipitation feature detection method has been used to describe the changes in precipitation regimes contributing to rainfall extremes. During the convectively active (suppressed) phases over the region, most of the precipitation is linked to enhanced (suppressed) widespread large-scale convective systems and suppressed (enhanced) isolated smaller-scale intense convective systems. Strong links between the slow-evolving RISV and rainfall extremes suggest that improved prediction of the likelihood of rainfall extremes may be feasible beyond the weather prediction time scales.

In this study, high-resolution quantitative precipitation forecasts (QPFs) in lagged runs with a cloud-resolving model are evaluated for three typhoons in the Philippines: Mangkhut (2018), Koppu (2015), and Melor (2015), hitting northern Luzon, central Luzon, and the middle section of the Philippine archipelago, respectively. In Part I of this study, the QPFs were verified using 56 gauge observations on land over the Philippines. Here, in Part II, they are verified against the Global Precipitation Measurement (GPM) satellite estimates (also covering nearby oceans), using categorical scores in the same way. For each typhoon, rainfall valid at a selected 24 h period and the whole event (48 or 72 h) is examined. For 24 h rainfall inside the short range (lead time ≤ 72 h), good QPFs (with a threat score of ≥0.2) were produced for Koppu at 200 mm by almost all runs, and at 100 mm by all runs for Mangkhut, but only 22% of the runs for Melor. At longer lead times, good QPFs at 100 mm were also produced by all runs for Koppu, half of the runs for Mangkhut, and only 1 out of 16 runs for Melor. For whole events (48 or 72 h), the QPFs were similarly the best for Koppu, followed by Mangkhut, and least ideal for Melor. The quality of the GPM data during the three typhoons was found to be generally good and suitable for QPF verification, and the results were more stable and, thus, more reliable for the assessment of bias. However, the threat scores using the GPM dropped lower at high thresholds, and the results could become different from those obtained against the gauges (Part I), suggesting a much higher skill. Thus, verification using rain gauges is still needed toward high thresholds, especially over mountain regions where satellite estimates tend to exhibit larger errors.