Evaluation of different convective schemes on simulation of thunderstorm event over Delhi by ARPS Model

Three difference cumulus parameterization schemes namely, Kain-Fritsch, New Kain-Fritsch and the Betts-Miller-Janjic are used to simulated convective rainfall associated with two thunderstorm events over Delhi by Advanced Regional Prediction Model (ARPS). An inter comparison of model simulated precipitation in respect of each convection scheme is made with reference to observed precipitation. The study shows that for the Delhi thunderstorm events, the Kain-Fritsch scheme provides more realistic results. This scheme is able to capture the temporal distribution of rainfall and the timely development of thunderstorm in both the cases. While the other two schemes fail to capture these features. However, the Kain-Fritsch scheme is found to overestimate the rainfall amount.

Simulation of mesoscale structure of thunderstorm using ARPS Model

In this paper mesoscale structures of two thunderstorm events over Delhi have been simulated using ARPS (Advanced Regional Prediction System) model. Numerical experiments were carried out using radiosonde data of Delhi and applying a potential temperature perturbation for triggering convective activity. The simulation exercise demonstrates strong updrafts and downdrafts associated with the thunderstorm cells, indicating the presence of very strong localized convection. The development and evolution of thunderstorm and propagation of associated precipitation zone are clearly brought out in this simulation study.

Precipitation simulation of synoptic scale systems over western Himalayan region using Advanced Regional Prediction System (ARPS) model

Operational weather prediction over western Himalayan region is a challenging job due to scarcity of data and complex topography that interacts with approaching weather system. Accurate prediction of complex weather phenomena requires dense data network which is difficult to establish in mountain due to complex terrain and hostile weather conditions over Himalaya. The alternate method to overcome this problem is by ingesting three-dimensional meteorological variables from global model’s analysis and forecast values as initial and lateral boundary conditions in meso-scale numerical models. Simultaneously, data assimilation is a potential tool in which non-conventional [satellite, radar and Automatic Weather Station (AWS)] and conventional (surface and upper air observations) data are ingested in the numerical models to generate high resolution and accurate initial fields for the initialization of the mesoscale model. In the present study, Advanced Regional Prediction System (ARPS) model has been used for the prediction of synoptic weather system known as Western Disturbance (WD) that affects the weather of western and central Himalaya during winter period (November – April).The ARPS model has been selected for this study because the model has its own objective analysis and quality control system. It has the capacity to ingest the satellite, Doppler weather radar data and other types of observations. Its assimilation system can also be used to overcome the problem of data scarcity in Himalayan region. In this study, initial and lateral boundary fields are taken from the T-80 spectral global model operationally used at National Centre for Medium Range Prediction (NCMRWF), Noida (UP), India. The global model’s analysis was taken as the initial condition and 24 hour’s interval forecasts as lateral boundary conditions. The model has been used for the simulation of few WDs for 96 hours (Four days). The comparison of ARPS simulation with T-80 forecast shows that the ARPS model could produce better results in respect of the circulation of WDs and hence it can be utilized for the operational weather prediction over the Indian region.

Optimizing and validating the Gravitational Process Path model for regional debris-flow runout modelling

Knowing the source and runout of debris flows can help in planning strategies aimed at mitigating these hazards. Our research in this paper focuses on developing a novel approach for optimizing runout models for regional susceptibility modelling, with a case study in the upper Maipo River basin in the Andes of Santiago, Chile. We propose a two-stage optimization approach for automatically selecting parameters for estimating runout path and distance. This approach optimizes the random-walk and Perla et al.'s (PCM) two-parameter friction model components of the open-source Gravitational Process Path (GPP) modelling framework. To validate model performance, we assess the spatial transferability of the optimized runout model using spatial cross-validation, including exploring the model's sensitivity to sample size. We also present diagnostic tools for visualizing uncertainties in parameter selection and model performance. Although there was considerable variation in optimal parameters for individual events, we found our runout modelling approach performed well at regional prediction of potential runout areas. We also found that although a relatively small sample size was sufficient to achieve generally good runout modelling performance, larger samples sizes (i.e. ≥80) had higher model performance and lower uncertainties for estimating runout distances at unknown locations. We anticipate that this automated approach using the open-source R software and the System for Automated Geoscientific Analyses geographic information system (SAGA-GIS) will make process-based debris-flow models more readily accessible and thus enable researchers and spatial planners to improve regional-scale hazard assessments.

Multivariate Tail Probabilities: Predicting Regional Pertussis Cases in Washington State

In disease modeling, a key statistical problem is the estimation of lower and upper tail probabilities of health events from given data sets of small size and limited range. Assuming such constraints, we describe a computational framework for the systematic fusion of observations from multiple sources to compute tail probabilities that could not be obtained otherwise due to a lack of lower or upper tail data. The estimation of multivariate lower and upper tail probabilities from a given small reference data set that lacks complete information about such tail data is addressed in terms of pertussis case count data. Fusion of data from multiple sources in conjunction with the density ratio model is used to give probability estimates that are non-obtainable from the empirical distribution. Based on a density ratio model with variable tilts, we first present a univariate fit and, subsequently, improve it with a multivariate extension. In the multivariate analysis, we selected the best model in terms of the Akaike Information Criterion (AIC). Regional prediction, in Washington state, of the number of pertussis cases is approached by providing joint probabilities using fused data from several relatively small samples following the selected density ratio model. The model is validated by a graphical goodness-of-fit plot comparing the estimated reference distribution obtained from the fused data with that of the empirical distribution obtained from the reference sample only.

Evaluation of CH4 Emission in Two Paddy Field Areas, Khonkaen and Ayutthaya, in Thailand

It is well known that submerged soils emit high levels of methane (CH4) due to oxygen deprivation and free iron oxide causing a quick reduction. However, there are other soil properties that control the reduction processes in soil, especially the amount of soil organic carbon (SOC). This study aimed to investigate the major factors controlling CH4 production potential (CH4PP) in Thai paddy fields. Two provinces, Ayutthaya, a clay soil region, and Khonkaen, a sandy soil region, were selected to represent a wide range of soil textures. Soil characteristic analysis pre- and post-incubation, and weekly gas detection in an incubation experiment over two months, was conducted. Stepwise multiple regression analysis was employed to analyze major soil factors controlling CH4PP. For the regional prediction of CH4PP, a map dataset of Ayutthaya and Khonkaen by the Land Development Department, Thailand, and a soil texture map (with intersected point data using the soil property map in ArcGIS) by OpenLandMap, were used. CH4PP was correlated with 1:10 pH, Fe2+, and water-soluble organic carbon (WSOC) measured after incubation. Although CH4PP showed no significant correlation with any soil properties measured before incubation, CH4PP was correlated with SOC, 1:10 electrical conductivity (EC), exchangeable ammonium (ExNH4), and sand content. It was thought that SOC and ExNH4 were related to organic matter decomposition, 1:10 EC was related to SO42− reduction and sand content was related to free oxides. Predicted regional CH4PP was similar in Ayutthaya and Khonkaen, although SOC, ExNH4 and 1:10 EC was higher, and sand content was lower in Ayutthaya than in Khonkaen. In both regions, the distribution of CH4PP corresponded to SOC, and CH4PP was lower with lower sand content and higher 1:10 EC. In clayey Ayutthaya, higher CH4PP was observed in the area with higher ExNH4. This indicates that soil properties other than soil texture and SOC influence CH4PP in the paddy fields in Thailand.

Optimizing and validating the Gravitational Process Path model for regional debris-flow runout modelling

Knowing the source and runout of debris-flows can help in planning strategies aimed at mitigating these hazards. Our research in this paper focuses on developing a novel approach for optimizing runout models for regional susceptibility modelling, with a case study in the upper Maipo river basin in the Andes of Santiago, Chile. We propose a two-stage optimization approach for automatically selecting parameters for estimating runout path and distance. This approach optimizes the random walk and Perla's two-parameter modelling components of the open-source Gravitational Process Path (GPP) modelling framework. To validate model performance, we assess the spatial transferability of the optimized runout model using spatial cross-validation, including exploring the model's sensitivity to sample size. We also present diagnostic tools for visualizing uncertainties in parameter selection and model performance. Although there was considerable variation in optimal parameters for individual events, we found our runout modelling approach performed well at regional prediction of potential runout areas. We also found that although a relatively small sample size was sufficient to achieve generally good runout modelling performance; larger samples sizes (i.e. ≥ 80) had higher model performances and lower uncertainties for estimating runout distances at unknown locations. We anticipate that this automated approach using open-source software R and SAGA-GIS will make process-based debris-flow models more readily accessible and thus enable researchers and spatial planners to improve regional-scale hazard assessments.

Toward a better multi-model ensemble prediction of East Asian and Australasian precipitation during non-mature ENSO seasons

An effective and reliable way for better predicting the seasonal Australasian and East Asian precipitation variability in a multi-model ensemble (MME) prediction system is newly designed, in relation to the performance of predicting El Niño-Southern Oscillation (ENSO) and its impact. While ENSO is a major predictability source of global and regional precipitation variation, the prediction skill of precipitation is not solely due to typical ENSO alone, of which variability and predictability exhibit strong seasonality. The first mode of ENSO variability has large variance with high prediction skill for boreal winter and small variance with low skill for spring and summer, while the second mode shows the opposite phase. The regional prediction skills for Australasian and East Asian precipitation also show such seasonal dependence, with low skill and large spread of individual models’ skills during the boreal spring to summer and high skill and small spread during winter. Using the individual models’ reproducibility of the association between ENSO and regional precipitation, the prediction skills of the MME with selected models can improve at regional levels, compared to those for all-inclusive MME, during boreal spring to summer. While typical ENSO as a predictability source may still dominate during boreal winter, consideration of complex ENSO structure and its diverse impact can lead to a better prediction of regional precipitation variability during non-mature phase of ENSO seasons.