T N Krishnamurti
Articles written in Journal of Earth System Science
Volume 115 Issue 2 April 2006 pp 185-201
In this observational/diagnostic study, we illustrate the time history of some important parameters of the surface energy balance during the life cycle of a single monsoon season. This chronology of the surface energy balance portrays the differential equilibrium state from the preonset phase to the withdrawal phase. This includes an analysis of the time history of base variables such as soil moisture, ground temperature, cloud cover, precipitation and humidity. This is followed by an analysis of the components of the surface energy balance where we note subtle changes in the overall balances as we proceed from one epoch of the monsoon to the next. Of interest here is the transition sequence: preonset, onset, break, revival, break, revival and withdrawal during the year 2001. Computations are all illustrated for a box over central India where the coastal effects were small, data coverage was not sparse and where the semi-arid land mass changes drastically to a lush green area. This region exhibited large changes in the components of surface energy balance. The principal results pertain to what balances the difference among the incoming short wave radiation (at the earth’s surface) and the long wave radiation exhibited by the ground. That difference is balanced by a dominant sensible heat flux and the reflected short wave radiation in the preonset stage. A sudden change in the Bowen ratio going from>1 to <1 is noted soon after the onset of monsoon. Thereafter the latent heat flux from the land surface takes an important role and the sensible heat flux acquires a diminishing role. We also examine the subtle changes that occur in the components of surface energy balance between the break and the active phases. The break phases are seen to be quite different from the preonset phases. This study is aimed to illustrate the major importance of moisture and clouds in the radiative transfer computations that are central to the surface energy balance during each epoch. These sensitivities (of moisture and clouds) have major consequences for weather and climate forecasts
Volume 115 Issue 5 October 2006 pp 529-555
In this study, the Florida State University Global Spectral Model (FSUGSM), in association with a high-resolution nested regional spectral model (FSUNRSM), is used for short-range weather forecasts over the Indian domain. Three-day forecasts for each day of August 1998 were performed using different versions of the FSUGSM and FSUNRSM and were compared with the observed fields (analysis) obtained from the European Center for Medium Range Weather Forecasts (ECMWF). The impact of physical initialization (a procedure that assimilates observed rain rates into the model atmosphere through a set of reverse algorithms) on rainfall forecasts was examined in detail. A very high nowcasting skill for precipitation is obtained through the use of high-resolution physical initialization applied at the regional model level. Higher skills in wind and precipitation forecasts over the Indian summer monsoon region are achieved using this version of the regional model with physical initialization.
A relatively new concept, called the ‘multimodel/multianalysis superensemble’ is described in this paper and is applied for the wind and precipitation forecasts over the Indian subcontinent. Large improvement in forecast skills of wind at 850 hPa level over the Indian subcontinent is shown possible through the use of the multimodel superensemble. The multianalysis superensemble approach that uses the latest satellite data from the Tropical Rainfall Measuring Mission (TRMM) and the Defense Meteorological Satellite Program (DMSP) has shown significant improvement in the skills of precipitation forecasts over the Indian monsoon region.
Volume 116 Issue 5 October 2007 pp 369-384
In the last thirty years great strides have been made by large-scale operational numerical weather prediction models towards improving skills for the medium range time-scale of 7 days.This paper illustrates the use of these current forecasts towards the construction of a consensus multimodel forecast product called the superensemble.This procedure utilizes 120 of the recent-past forecasts from these models to arrive at the training phase statistics. These statistics are described by roughly 107 weights.Use of these weights provides the possibility for real-time medium range forecasts with the superensemble.We show the recent status of this procedure towards real-time forecasts for the Asian summer monsoon.The member models of our suite include ECMWF,NCEP/EMC, JMA,NOGAPS (US Navy),BMRC,RPN (Canada)and an FSU global spectral forecast model. We show in this paper the skill scores for day 1 through day 6 of forecasts from standard variables such as winds,temperature,500 hPa geopotential height,sea level pressure and precipitation.In all cases we noted that the superensemble carries a higher skill compared to each of the member models and their ensemble mean.The skill matrices we use include the RMS errors,the anomaly correlations and equitable threat scores.For many of these forecasts the improvements of skill for the superensemble over the best model was found to be quite substantial.This real-time product is being provided to many interested research groups.The FSU multimodel superensemble,in real- time,stands out for providing the least errors among all of the operational large scale models.
Volume 120 Issue 1 February 2011 pp 27-52
Realistic simulation/prediction of the Asian summer monsoon rainfall on various space–time scales is a challenging scientific task. Compared to mid-latitudes, a proportional skill improvement in the prediction of monsoon rainfall in the medium range has not happened in recent years. Global models and data assimilation techniques are being improved for monsoon/tropics. However, multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical forecasting in terms of making a consensus forecast and handling model uncertainties. As major centers are exchanging model output in near real-time, MME is a viable inexpensive way of enhancing the forecasting skill and information content. During monsoon 2008, on an experimental basis, an MME forecasting of large-scale monsoon precipitation in the medium range was carried out in real-time at National Centre for Medium Range Weather Forecasting (NCMRWF), India. Simple ensemble mean (EMN) giving equal weight to member models, bias-corrected ensemble mean (BCEMn) and MME forecast, where different weights are given to member models, are the products of the algorithm tested here. In general, the aforementioned products from the multi-model ensemble forecast system have a higher skill than individual model forecasts. The skill score for the Indian domain and other sub-regions indicates that the BCEMn produces the best result, compared to EMN and MME. Giving weights to different models to obtain an MME product helps to improve individual member models only marginally. It is noted that for higher rainfall values, the skill of the global model rainfall forecast decreases rapidly beyond day-3, and hence for day-4 and day-5, the MME products could not bring much improvement over member models. However, up to day-3, the MME products were always better than individual member models.
Volume 122 Issue 4 August 2013 pp 991-1004
The purpose of this study is to address prediction of the start date and the duration of breaks in the summer monsoon rains using multi-model superensemble. The availability of datasets from the ‘observing system research and predictability experiment (THORPEX)’ initiated a forecast data archive, called THORPEX interactive grand global ensemble (TIGGE), makes it possible to use forecasts from a suite of individual ensemble prediction systems (member models) and to construct multi-model superensemble forecasts that are designed to remove the collective bias errors of the suite of models. Precipitation datasets are important for this study, we have used high resolution daily gridded rainfall dataset of India Meteorological Department (IMD), in addition to rainfall estimates from tropical rainfall microwave mission (TRMM) satellite and the CPC morphing technique (CMORPH). The scientific approach of this study entails the use of a multi-model superensemble for forecast and to verify against the rainfall information during a training phase, as well as during a forecast phase. We examine the results of forecasts out to day-10 and ask how well do forecast strings of day-1 through day-10 handle the prediction of the onset and duration of the breaks in the summer monsoon rains. Our results confirm that it is possible to predict the onset of a dry spell, around week in advance from the use of the multi-model superensemble and a suite of TIGGE models.We also examine trajectories of the parcels arriving in India in such forecasts from member models and from the multi-model superensemble to validate the arrival of descending dry desert air from the Arabian region during the dry spells and its mode of transition from wet spell. Some phenological features such as a shift in the latitude of the tropical easterly jet and changes in its intensity during break periods are additional observed features that are validated from the history of multi-model superensemble forecasts. Invariably this multi-model superensemble performs better than any single model in proving the better forecasts during our experiment period.
Volume 122 Issue 5 October 2013 pp 1173-1182
Indian monsoon is an important component of earth’s climate system. Daily rainfall data for longer period is vital to study components and processes related to Indian monsoon. Daily observed gridded rainfall data covering both land and adjoining oceanic regions are required for numerical model validation and model development for monsoon. In this study, a new gridded daily Indian rainfall dataset at 1° × 1° latitude/longitude resolution covering 14 monsoon seasons (1998–2011) are described. This merged satellite gauge rainfall dataset (NMSG) combines TRMM TMPA rainfall estimates with gauge information from IMD gridded data. Compared to TRMM and GPCP daily rainfall data, the current NMSG daily data has more information due to inclusion of local gauge analysed values. In terms of bias and skill scores this dataset is superior to other daily rainfall datasets. In a mean climatological sense and also for anomalous monsoon seasons, this merged satellite gauge data brings out more detailed features of monsoon rainfall. The difference of NMSG and GPCP looks significant. This dataset will be useful to researchers for monsoon intraseasonal studies and monsoon model development research.