Modelling of Indian Monsoon Rainfall Series by Univariate Box-Jenkins Type of Models

The time domain approach, i.e. Autoregressive (AR) processes, of time series analysis is applied to the monsoon rainfall series of India and its two major regions, viz. North-West India and Central India. Since the original time series shows no modelable structure due to the presence of high interannual variability, a 3-point running filter is applied before exploring and fitting appropriate stochastic models. Out of several parsimonious models fitted, AR(3) is found to be most suitable. The usefulness of this fitted model is validted on an independent datum of 18 years and some skill has been noted. These models therefore can be used for low skill higher lead time forecasts of monsoon. Further the forecasts produced through such models can be combined with other forecasts to increase the skill of monsoon forecasts.

Analysis of a Heavy Rainfall Event over Beijing During 21-22 July2012 Based on High Resolution Model Analyses and Forecasts

The heaviest rainfall over 61 yr hit Beijing during 21-22 July 2012.Characterized by great rainfall amount and intensity,wide range,and high impact,this record-breaking heavy rainfall caused dozens of deaths and extensive damage.Despite favorable synoptic conditions,operational forecasts underestimated the precipitation amount and were late at predicting the rainfall start time.To gain a better understanding of the performance of mesoscale models,verification of high-resolution forecasts and analyses from the WRFbased BJ-RUCv2.0 model with a horizontal grid spacing of 3 km is carried out.The results show that water vapor is very rich and a quasi-linear precipitation system produces a rather concentrated rain area.Moreover,model forecasts are first verified statistically using equitable threat score and BIAS score.The BJ-RUCv2.0forecasts under-predict the rainfall with southwestward displacement error and time delay of the extreme precipitation.Further quantitative analysis based on the contiguous rain area method indicates that major errors for total precipitation（〉 5 mm h^（-1）） are due to inaccurate precipitation location and pattern,while forecast errors for heavy rainfall（〉 20 mm h^（-1）） mainly come from precipitation intensity.Finally,the possible causes for the poor model performance are discussed through diagnosing large-scale circulation and physical parameters（water vapor flux and instability conditions） of the BJ-RUCv2.0 model output.

Effects of Doubled Carbon Dioxide on Rainfall Responses to Radiative Processes of Water Clouds

The effects of doubled carbon dioxide on rainfall responses to radiative processes of water clouds are investigated in this study.Two groups of two-dimensional cloud-resolving model sensitivity experiments with regard to pre-summer heavy rainfall around the summer solstice and tropical rainfall around the winter solstice are conducted and their five-day averages over the model domain are analyzed.In the presence of radiative effects of ice clouds,doubled carbon dioxide changes pre-summer rainfall from the decrease associated with the enhanced atmospheric cooling to the increase associated with the enhanced infrared cooling as a result of the exclusion of radiative effects of water clouds.Doubled carbon dioxide leads to the reduction in tropical rainfall,caused by the removal of radiative effects of water clouds through the suppressed infrared cooling.In the absence of radiative effects of ice clouds,doubled carbon dioxide changes pre-summer rainfall from the increase associated with the strengthened atmospheric warming to the decrease associated with the weakened release of latent heat caused by the elimination of radiative effects of water clouds.The exclusion of radiative effects of water clouds increases tropical rainfall through the strengthened infrared cooling,which is insensitive to the change in carbon dioxide.

12 HOUR PRECIPITATION FORECASTS FOR NAIROBI,KENYA

Radiosonde profiles of temperature and dewpoint temperature from one station are used to forecast 12-h precipita- tion over Nairobi,Kenya.The forecast scheme is based on statistical regression modelling.Four predictors are derived from data to use in a prognostic equation to get 12-h precipitation forecast.Observed and predicted rainfall values are plotted on a graph against time.Forecast verification shows that the forecasts are positively correlated with observations.