N. Borah

Improved Spread–Error Relationship and Probabilistic Prediction from the CFS-Based Grand Ensemble Prediction System

AbstractThis study describes an attempt to overcome the underdispersive nature of single-model ensembles (SMEs). As an Indo–U.S. collaboration designed to improve the prediction capabilities of models over the Indian monsoon region, the Climate Forecast System (CFS) model framework, developed at the National Centers for Environmental Prediction (NCEP-CFSv2), is selected. This article describes a multimodel ensemble prediction system, using a suite of different variants of the CFSv2 model to increase the spread without relying on very different codes or potentially inferior models. The SMEs are generated not only by perturbing the initial condition, but also by using different resolutions, parameters, and coupling configurations of the same model (CFS and its atmosphere component, the Global Forecast System). Each of these configurations was created to address the role of different physical mechanisms known to influence error growth on the 10–20-day time scale. Last, the multimodel consensus forecast is de...

Development and Evaluation of an Objective Criterion for the Real-Time Prediction of Indian Summer Monsoon Onset in a Coupled Model Framework

AbstractThis study reports an objective criterion for the real-time extended-range prediction of monsoon onset over Kerala (MOK), using circulation as well as rainfall information from the 16 May initial conditions of the Grand Ensemble Prediction System based on the coupled model CFSv2. Three indices are defined, one from rainfall measured over Kerala and the others based on the strength and depth of the low-level westerly jet over the Arabian Sea. While formulating the criterion, the persistence of both rainfall and low-level wind after the MOK date has been considered to avoid the occurrence of “bogus onsets” that are unrelated to the large-scale monsoon system. It is found that the predicted MOK date matches well with the MOK date declared by the India Meteorological Department, the authorized principal weather forecasting agency under the government of India, for the period 2001–14. The proposed criterion successfully avoids predicting bogus onsets, which is a major challenge in the prediction of MOK...

A bias-correction and downscaling technique for operational extended range forecasts based on self organizing map

If a coarse resolution dynamical model can well capture the large-scale patterns even if it has bias in smaller scales, the spatial information in smaller domains may also be retrievable. Based on this hypothesis a method has been proposed to downscale the dynamical model forecasts of monsoon intraseasonal oscillations in the extended range, and thus reduce the forecast spatial biases in smaller spatial scales. A hybrid of clustering and analog technique, used in a self organizing map (SOM)-based algorithm, is applied to correct the bias in the model predicted rainfall. The novelty of this method is that the bias correction and downscaling could be done at any resolution in which observation/reanalysis data is available and is independent of the model resolution in which forecast is generated. A set of composite pattern of rainfall is identified by clustering the high resolution observed rainfall using SOM. These set of composite patterns for the clustered days in each cluster centers or nodes are saved and the model forecasts for any day are compared with these patterns. The closest historical pattern is identified by calculating the minimum Euclidean distance between the model rainfall forecast and the observed clustered pattern and is termed as the bias corrected SOM-based post-processed forecast. The bias-corrected and the SOM-based reconstructed forecasts are shown to improve the annual cycle and the skill of deterministic as well as probabilistic forecasts. Usage of the high resolution observational data improves the spatial pattern for smaller domain as seen from a case study for the Mahanadi basin flood during September 2011. Thus, downscaling and bias correction are both achieved by this technique.