Search Results (6)

To evaluate tropical cyclone forecasting on synoptic timescale, tracking and intensity are used. On subseasonal to seasonal (S2S) timescale, what aspects of tropical cyclones should be predicted and how to evaluate forecasting skills still remain open questions. Following our previous work, which proposed using daily tropical cyclone probability (DTCP) as a measure of tropical cyclone activity and the debiased Brier skill score (DBSS) to evaluate tropical cyclone forecasting on S2S timescale, the present research investigates the influence of several factors that may influence the use of DTCP and the DBSS framework. These factors are the forecast time window, tropical cyclone influence radius, evaluation region, forecast sample, and how the Brier score for the reference climate forecast is computed. The influence of these factors is discussed based on the output of the S2S prediction project database and comparison of the DBSS when the above factors are changed individually. Changes in the forecast time window, evaluation region, and tropical cyclone influence radius can change the DTCP. The larger the tropical cyclone influence radius and the longer the forecast time window, the larger the DTCP will be. However, the spatially averaged DBSS changes very little. Using estimated Brier score for reference climate forecast can cause variation due to limited forecast samples. It is recommended to use the theoretical value of the Brier score for reference climate forecasting, instead of its estimation. Full article

Development of ocean measurement technologies can improve monitoring of the global Ocean Heat Content (OHC) and Heat Storage Rate (HSR) that serve as early-warning indices for climate-critical circulation processes such as the Atlantic Meridional Overturning Circulation and provide real-time OHC assessments for tropical cyclone forecast models. This paper examines the potential of remotely measuring ocean temperature profiles using a simulated Brillouin lidar for calculating ocean HSR. A series of data analysis (‘Nature’) and Observational Systems Simulation Experiments (OSSEs) were carried out using 26 years (1992–2017) of daily mean temperature and salinity outputs from the ECCOv4r4 ocean circulation model. The focus of this study is to compare various OSSEs carried out to measure the HSR using a simulated Brillouin lidar against the HSR calculated from the ECCOv4r4 model results. Brillouin lidar simulations are used to predict the probability of detecting a return lidar signal under varying sampling strategies. Correlations were calculated for the difference between sampling strategies. These comparisons ignore the measurement errors inherent in a Brillouin lidar. Brillouin lidar technology and instruments are known to contain numerous, instrument-dependent errors and remain an engineering challenge. A significant decrease in the ability to measuring global ocean HSRs is a consequence of measuring ocean temperature from nadir-pointing instruments that can only take measurements along-track. Other sources of errors include the inability to fully profile ocean regions with deep mixed layers, such as the Southern Ocean and North Atlantic, and ocean regions with high light attenuation levels. Full article

Predicting heavy rainfall events associated with Tropical Cyclones (TCs) and atmospheric disturbances in Thailand remains challenging. This study introduces a novel approach to enhance forecasting precision by utilizing the coupled Weather Research and Forecasting (WRF) and Regional Oceanic Model (ROMS), known as WRF-ROMS. We aim to identify the optimal combination of microphysics (MP) and cumulus (CU) parameterization schemes. Three CU schemes, namely, Betts-Miller-Janjic (BMJ), Grell 3D Ensemble (G3), and Kain-Fritsch (KF), along with three MP schemes, namely, Eta (ETA), Purdue Lin (LIN), and WRF Single-moment 3-class (WSM3), are selected for the sensitivity analysis. Seven instances of heavy (35.1–90.0 mm) to violent (>90.1 mm) rainfall in Thailand, occurring in 2020 and associated with tropical storms and atmospheric disturbances, are simulated using all possible combinations of the chosen physics schemes. The simulated rain intensities are compared against observations from the National Hydroinformatics Data Center. Performance was assessed using the probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) metrics. While the models performed well for light (0.1–10.0 mm) to moderate (10.1–35.0 mm) rainfall, forecasting heavy rainfall remained challenging. Certain parameter combinations showed promise, like BMJ and KF with LIN microphysics, but challenges persisted. Analyzing density distribution of daily rainfall, we found effective parameterizations for different sub-regions. Our findings emphasize the importance of tailored parameterizations for accurate rainfall prediction in Thailand. This customization can benefit water resource management, flood control, and disaster preparedness. Further research should expand datasets, focusing on significant heavy rainfall events and considering climate factors, for example, the Madden-Julian Oscillation (MJO) for extended-range forecasts, potentially contributing to sub-seasonal and seasonal (S2S) predictions. Full article

Rainfall events are known to be driven by various synoptic disturbances or dominant processes in the atmosphere. In spite of the diverse atmospheric contributions, the assumption of homogeneity is commonly adopted when a hydrological frequency analysis is conducted. This study examines how the dominant processes, particularly the landfalling tropical cyclones (TCs) and non-TC events, have various effects to the characteristics of rainfall in South Korea. With rainfall data from the fifty-nine weather stations spread across the country, the multiple contributions of the TC and non-TC rainfall to the relative amount of rainfall, duration, intensity and maximum rainfall, on a seasonal and monthly scale, are first explored in this study. For the second objective, suitable probability distributions for the TC and non-TC time series are identified potentially for a synthetic analysis. Our results indicate that TCs cause a heterogeneous spatial distribution in the rainfall characteristics over the gauge networks particularly in the southern and eastern coastal areas. Some gauges in these areas attribute a significant portion of their amount and annual maximum rainfall to landfalling TCs. The results also show that the Pearson Type III distribution best represents the non-TC wet-day series, while the TC wet-day series can be represented by various distributions including the Weibull and Gamma distributions. From the analysis, we present how the characteristics of TCs differ from non-TCs with the emphasis on the need to consider their individual effects when conducting synthetic analyses. Full article

The complexity of solar radiation fluctuations received on the ground is nowadays of great interest for solar resource in the context of climate change and sustainable development. Over tropical maritime area, there are small inhabited islands for which the prediction of the solar resource at the daily and infra-daily time scales are important to optimize their solar energy systems. Recently, studies show that the theory of the information is a promising way to measure the solar radiation intermittency. Kolmogorov complexity (KC) is a useful tool to address the question of predictability. Nevertheless, this method is inaccurate for small time series size. To overcome this drawback, a new encoding scheme is suggested for converting hourly solar radiation time series values into a binary string for calculation of Kolmogorov complexity (KC-ES). To assess this new approach, we tested this method using the 2004–2006 satellite hourly solar data for the western part of the Indian Ocean. The results were compared with the algorithmic probability (AP) method which is used as the benchmark method to compute the complexity for short string. These two methods are a new approach to compute the complexity of short solar radiation time series. We show that KC-ES and AP methods give comparable results which are in agreement with the physical variability of solar radiation. During the 2004–2006 period, an important interannual SST (sea surface temperature) anomaly over the south of Mozambique Channel encounters in 2005, a strong MJO (Madden–Julian oscillation) took place in May 2005 over the equatorial Indian Ocean, and nine tropical cyclones crossed the western part of the Indian Ocean in 2004–2005 and 2005–2006 austral summer. We have computed KC-ES of the solar radiation time series for these three events. The results show that the Kolmogorov complexity with suggested encoding scheme (KC-ES) gives competitive measure of complexity in regard to the AP method also known as Solomonoff probability. Full article

Almost every year, north Atlantic tropical cyclones (TCs) are responsible for significant socioeconomic losses across the Mid-Atlantic USA. However, the extent to which TC activity contributes to the changes in the probability distributions of the extreme precipitation have not yet been comprehensively characterized for this region. In this study, a quantile regression method was used to investigate the trends of the lower (τ = 0.2) and upper (τ = 0.8) quantiles of annual and seasonal daily maximum precipitation series for the region using the station-based daily precipitation data for the period 1950–2011. Results show that the rates of changes in the upper quantile have greatly strengthened for the region. Analysis of the spatial pattern of the lower and upper quantile trends for TC and non-TC extreme precipitation series shows that trends have larger magnitudes in most of the sites for TC precipitation series as compared with the non-TC precipitation series for both the lower and upper quantiles. Additionally, the highest trends are observed in the upper quantile for TC time series indicating that TC precipitation is contributing more to the upper tails of the extreme precipitation distribution as compared to the non-TC precipitation. Results from this study have implications for the improved design and reassessment of flood-controlling infrastructure. Full article