The effect of Indian Ocean warming on the Indian Monsoon: An atmospheric model study

The results from an atmospheric modeling study using the Florida State University Global Spectral Model indicate that, in years such as 1997 when the Indian Ocean SSTs are large, the Indian monsoon exhibits a typical behaviour. During that year, an extended shift of the tropical convergence zone towards the north played a role in the regional Hadley cell anomalies. The local warm boundary conditions in the northwestern Indian Ocean aided the high rainfall anomaly in Western India during the model simulations. The upper level structure, exhibited in terms of the global velocity potential is slightly shifted east for 1997, but with the correct sign. This structure shows regions of convergence over Indonesia where severe drought had occurred. The performance of the model rainfall over the equatorial Indian Ocean was uncanny for most seasons studied. Overall, the model performed best over the oceanic regions.

A model study of the 30-50 day oscillation In the tropical atmosphere

Using a global spectral model with wave-CISK formulation we have generated an eastward de which. Resembles the observed 30-50 day mode. This has a scale of global wave number one and two years structure in the vertical. It has the structure of a composite of Kelvin and Rossby waves. This composite system moves eastwards. We have also studied a linear two-level analytical model to understand the nonlinear spectral model response. In the linear as well as in the nonlinear spectral model, as we Increase the moisture availability factor the speeds of the waves decrease. In the linear model this speed is found to be independent of drag for all types of waves. In the nonlinear spectral model for a given drag there is a critical value of the moisture availability factor for which the wave becomes stationary and beyond which even shows westward propagation. Thus both moisture availability and nonlinearity appear to contribute to the slow eastward speed of the equatorial 30-50 day mode.

Medium range prediction of tropical cyclogenesis of intense vortices over Indian Seas by a Global Spectral Model

The performance of a Global Spectral Model (T-80) operational at the National Centre for Medium Range Weather Forecasting (NCMRWF), New Delhi in predicting the cyclogenesis of six tropical cyclones over Indian Seas formed during 1995-96 has been evaluated. It has been found that the model has the capability to predict cyclogenesis in wind field at least 72 hours in advance although the positions of predicted vortices are seen to be displaced from those of analysed ones in some cases. The quantitative estimates of the atmospheric conditions favourable for cyclogenesis also confirm the conclusions drawn from the qualitative analysis of cyclogenesis predictions of the model in terms of appearance of cyclonic circulation. It also follows from this analysis that the predicted circulations at the cyclogenesis stage are in general more intense and stronger as compared to the corresponding analysis in terms of wind and mass fields. On examining the model systematic errors of prediction it is found that the model has a clear bias for predicting more intense vortex during genesis and weakening stages. On the order hand it predicts relatively less intense vortex during intensification process.

Some dynamical aspects of meso-scale rainfall events as revealed by physical initialization

In recent years, physical initialization has emerged as a powerful tool to improve initial state of dynamical model during assimilation phase. This improved initial state at high resolution global spectral model is able to provide a tropical meso-scale coverage. In this paper, model out-put is used to study some dynamical aspects of meso-scale rainfall events. Major findings of this study are : (i) Meso-scale rainfall event carries a distinct dynamic structure in vertical profiles of divergence and vertical upward motion, (ii) Meso-scale event exhibits a large diurnal variation in these vertical profiles and (iii) Vertical motion field of meso-scale organisation appears to play a significant role in tropical storm formation.

Can we use precipitation isotope outputs of isotopic general circulation models to improve hydrological modeling in large mountainous catchments on the Tibetan Plateau?

Issues related to large uncertainty and parameter equifinality have posed big challenges for hydrological modeling in cold regions where runoff generation processes are particularly complicated. Tracer-aided hydrological models that integrate the transportation and fractionation processes of water stable isotope are increasingly used to constrain parameter uncertainty and refine the parameterizations of specific hydrological processes in cold regions. However, the common unavailability of site sampling of spatially distributed precipitation isotopes hampers the practical applications of tracer-aided models in large-scale catchments. This study, taking the precipitation isotope data (isotopes-incorporated global spectral model – isoGSM) derived from the isotopic general circulation models (iGCMs) as an example, explored its utility in driving a tracer-aided hydrological model in the Yarlung Tsangpo River basin (YTR; around 2×105 km2, with a mean elevation of 4875 m) on the Tibetan Plateau (TP). The isoGSM product was firstly corrected based on the biases between gridded precipitation isotope estimates and the limited site sampling measurements. Model simulations driven by the corrected isoGSM data were then compared with those forced by spatially interpolated precipitation isotopes from site sampling measurements. Our results indicated that (1) spatial precipitation isotopes derived from the isoGSM data helped to reduce modeling uncertainty and improve parameter identifiability in a large mountainous catchment on the TP, compared to a calibration method using discharge and snow cover area fraction without any information on water isotopes; (2) model parameters estimated by the corrected isoGSM data presented higher transferability to nested subbasins and produced higher model performance in the validation period than that estimated by the interpolated precipitation isotope data from site sampling measurements; (3) model calibration forced by the corrected isoGSM data successfully rejected parameter sets that overestimated glacier melt contribution and gave more reliable contributions of runoff components, indicating the corrected isoGSM data served as a better choice to provide informative spatial precipitation isotope than the interpolated data from site sampling measurements at the macro scale. This work suggested plausible utility of combining isoGSM data with measurements, even from a sparse sampling network, in improving hydrological modeling in large high mountain basins.

818Prediction of health effects of Asian dust using the MASINGAR in Japan

Background Health effects of cross-border air pollutants and Asian dust are of significant concern in Japan. Currently, models predicting arrival of aerosols have not investigated the association between arrival predictions and health effects. We investigated the association between subjective health symptoms and data acquired from the Japan Meteorological Agency's (JMA's) the Model of Aerosol Species in the Global Atmosphere (MASINGAR) aerosol model with the objective of ascertaining if the data could be applied for predicting health effects. Methods Subjective symptom scores were collected using self-administered questionnaires and used with JMA model’s surface concentration data to conduct a risk evaluation using multiple linear mixed model, during 2013 to 2015. Altogether, 160 individuals provided 16226 responses. Data regarding climate (temperature, humidity, and atmospheric pressure) and environmental factors (NO2, SO2 and Ox) were used as covariates. We calculated the association between the surface dust concentration and symptoms. Results A strong association was also observed for nasal and cough symptoms (P for trend < 0.001). The differences in scores of nasal symptoms (sneezing and runny) of the highest quartile [Q4] vs. the lowest [Q1] were 0.039 (95% confidence interval (CI): 0.02–0.01, p < 0.05) and 0.046 (95% CI: 0.002–0.02, p < 0.05), respectively. The differences in scores of cough symptoms were 0.036 (95% confidence interval (CI): 0.002–0.01, p < 0.05). Conclusions This study suggests that predictive models for pollutants’ arrival can be used to capability to foresee and possibly prevent the health impact of long range transport of air pollutants, recommending the potential role of aerosol forecast models in health care. MASINGAR is Global Spectral Model (GSM), this have the potential that can contribute in health predictions all over the world. Key messages Asian dust, Health forecast, Allergic symptom

Response of a Global Spectral Model for Simulation of Indian Summer Monsoon Rainfall

The present work highlights response of a global spectral model T80L18 with respect to Indian summer monsoon rainfall (ISMR) during 8 years period of 1996-2003. The model performance is evaluated for day-1, day-3 and day-4 retrospective 24-hour accumulated rainfall forecasts from 0300 UTC to the next day 0300 UTC using in-situ rainfall observations of 4491 stations. The model performance is evaluated by assessing: (i) percentage departure and root mean square error (RMSE) of seasonal rainfall forecast, (ii) coefficient of variation (CoV) of seasonal rainfall forecast and observation, along with percentage departure of monthly rainfall forecast and (iii) model performance during a drought and a normal year of 2002 and 2003, respectively. Generally, it is noted that the T80L18 model underestimated high rainfall and overestimated low rainfall, however, with increasing forecast duration prediction over low rainfall areas improved. The model RMSE over central and western India is found to increase with increasing forecast duration; however, the same was found to decrease over Jammu and Kashmir. The CoV of day-1 rainfall forecast is found to be low over all India in comparison to the observed data. In the case of model performance evaluation during a drought and a normal year of 2002 and 2003, it is noted that the model produced higher rainfall over the rainfall deficit regions of observed distribution; whereas the heaviest observed rainfall region (>250 cm) is not well resolved by the model. In general, the T80L18 model performance is noted to be better over central India for mean seasonal rainfall prediction.

Using Weather Pattern Recognition to Classify and Predict Summertime Heavy Rainfall Occurrence over the Upper Nan River Basin, Northwestern Thailand

In this study, self-organizing maps in combination with K-means clustering are used to objectively classify the anomalous weather patterns (WPs) associated with the summertime [May–June (MJ) and July–August–September (JAS)] heavy rainfall days during 1979–2007 over the Upper Nan River basin, northwestern Thailand. The results show that in MJ, intensive rains are mainly brought by the remarkable enhancement of the westerly summer monsoon. Meanwhile, westward-propagating tropical disturbances including tropical cyclones are the primary factors that reproduce heavy rainfall over the Upper Nan in JAS. These results also suggest that the occurrence time of local heavy rainfall is strongly related to the seasonal transition of the summer monsoon over the Indochina Peninsula. The classification results are then implemented with the perfect prognosis and analog method to predict the occurrence (yes/no) of heavy rainfall days over the studied basin in summer 2008–17 using prognostic WPs from the operational Japan Meteorological Agency Global Spectral Model (GSM). In general, the forecast skill of this approach up to 3-day lead times is significantly improved, in which the method not only outperforms GSM with the same forecast ranges, but also its 3-day forecast is better than the 1–2-day forecasts from GSM. However, the false alarms ratio is still high, particularly in JAS. Nevertheless, it is expected that the new approach will provide warning and useful guidance for decision-making by forecasters or end-users engaging in water management and disaster prevention activities.