Modelling for Science, for a better future - some recent outcomes
An Assessment of Optimality of Observations in High-resolution Weather Forecasting
by Prashant Goswami and V. Rakesh
Data assimilation is a critical component for short-range weather forecasting; a number of algorithms have been developed and applied for assimilation of different kind of observations. However, an important but less explored question is the (optimal) amount of observation for maximum improvement in forecasts through data assimilation. Because the meteorological fields at different spatial and temporal resolutions are not necessarily mutually independent, indefinite increase in resolution of observations may be ineffective; thus data optimality in this sense can be defined as the maximum resolution of observation beyond which no appreciable improvement occurs due to assimilation of data. Based on forecasts of seven events over a complex terrain (urban location, Delhi) with different combinations of observations, we show that improvement in forecast skill does not saturate even with assimilation of observations a few kilometers (<10 km) apart. The improvement due to assimilation of data from each of the profilers is appreciable; however, the impact was generally the highest for assimilation of data from all the four profilers. Applicable strategies for observation system design over high-impact areas are discussed.
source: http://link.springer.com/article/10.1007/s00024-015-1155-1
Impact of data assimilation on high-resolution rainfall forecasts: A spatial, seasonal, and category analysis
by Rakesh V and Prashant Goswami
In a limited area model (LAM), the impact of data assimilation is likely to depend on the background state through lateral boundary forcing; this may introduce certain seasonality in the impact of data assimilation on rainfall forecasting. It is also likely that the impact of data assimilation on forecasts will have certain spatial variability. Finally, owing to the convective nature of rainfall and the roles of parameterization scheme, the impact of data assimilation may depend on the category (intensity) of rainfall. Here these aspects for rainfall forecasts at high resolution were examined. Using a LAM (An advanced version of Weather Research and Forecasting Model), we have carried out twin simulations with and without data assimilation; the simulations without data assimilation are used as the benchmark for assessing the impact of data assimilation. Analysis of simulations for 40 sample days distributed over the years 2012–2014 over Karnataka (southern state in India) is carried out to estimate impact of data assimilation. Various statistical measures show that data assimilation improved the rainfall prediction in most cases; however, there is also strong seasonality and location dependence in impact of data assimilation. Our results also show that improvement due to data assimilation is higher/lower for lower/higher rainfall categories. Analysis shows that the cases where the initial states with data assimilation depart strongly from the first guess generally result in less or even negative impact. It is pointed out that the results have important implications in design of observation system and assessment of impact of forecasts.
Source: http://onlinelibrary.wiley.com/doi/10.1002/2014JD022383/abstract
Evaluation of high resolution rainfall forecasts over Karnataka for the 2011 southwest and northeast monsoon seasons
by V. Rakesh, P. Goswami and V. S. Prakash
Evaluation of real-time rainfall forecast at the hobli (village cluster ∼10 km2) level from an atmospheric mesoscale model over Karnataka (located at southwest India, with nearly 56% of the workforce engaged in agricultural activities) for the southwest (June–September) and northeast (October–December) monsoon seasons of 2011 is presented in this study. The forecast system has been operationally implemented through data assimilation from a number of local observatories for effective applications as well as for proof of concept. A highlight of the study is the validation of the rainfall forecasts against observations at comparable resolution established by Karnataka State Natural Disaster Monitoring Centre, Government of Karnataka (KSNDMC). An evaluation of the forecast skill against daily observed rainfall is presented. A number of statistical evaluations show that the forecasts have enough skill to be useful for end users. A few areas of systematic bias and relatively higher forecast error were identified for further improvement in forecast skill.
Source: http://onlinelibrary.wiley.com/doi/10.1002/met.1438/abstract
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