Prof. Tiruvalam N. Krishnamurti Profile

Prof. Tiruvalam N. Krishnamurti

at Florida State Univ

SPIE Involvement:

Author | Editor

Proceedings Article | 23 October 2018 Paper

Association between upper level diffluence in the Tropical Easterly Jet and the formation of the strongest Atlantic hurricanes in recent years

T. N. Krishnamurti, Nirupam Karmakar, Vasubandhu Misra, Bappaditya Nag, Dipak Sahu, Sarvesh Dubey, Ziad Haddad

Proceedings Volume 10782, 1078206 (2018) https://doi.org/10.1117/12.2500287

KEYWORDS: Troposphere, Lead, Temporal resolution, Wave propagation, Iris, Nomenclature, Atmospheric sciences, Climatology, Heart, Data centers

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Proceedings Article | 3 May 2016 Paper

Analysis and evaluation of Observing System Simulation Experiments (OSSEs) forecast data for Indian summer monsoon

Medha Deshpande, P. Mukhopadhyay, Michiko Masutani, Zaizhong Ma, Lars Peter Riishojgaard, Michael Hardesty, Dave Emmitt, T. N. Krishnamurti, B. Goswami

Proceedings Volume 9882, 98821P (2016) https://doi.org/10.1117/12.2223656

KEYWORDS: Humidity, Clouds, Error analysis, Doppler effect, LIDAR, Atmospheric modeling, Satellites, Spatial resolution, Convection, Systems modeling

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Proceedings Article | 29 November 2012 Paper

Impacts of enhanced CCN on the organization of convection and recent reduced counts of monsoon depressions

T. Krishnamurti, Andrew Martin, Ruby Krishnamurti, Anu Simon, Aype Thomas, Vinay Kumar

Proceedings Volume 8529, 85290E (2012) https://doi.org/10.1117/12.979717

KEYWORDS: Aerosols, Clouds, Pollution, Convection, Atmospheric modeling, Meteorology, Troposphere, MODIS, Data modeling, Satellites

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Monsoon depressions, that form during the Indian summer monsoon season (June to September) are known to be baroclinic disturbances (horizontal scale 2000 to 3000 km) and are driven by deep convection that carries a very large vertical slope towards cold air aloft in the upper troposphere. Deep convection is nearly always organized around the scale of these depressions. In the maintenance of the monsoon depression the generation of eddy kinetic energy on the scale of the monsoon depression is largely governed by the “in scale” covariance of heating and temperature and of vertical velocity and temperature over the region of the monsoon depression. There are normally about 6 to 8 monsoon depressions during a summer monsoon season. Recent years 2009, 2010 and 2011 saw very few (around 1, 0 and 1 per season respectively). The best numerical models such as those from ECMWF and US (GFS) carried many false alarms in their 3 to 5 day forecasts, more like 6 to 8 disturbances. Even in recent years with fewer observed monsoon depressions a much larger number of depressions is noted in ECMWF forecasts. These are fairly comprehensive models that carry vast data sets (surface and satellite based), detailed data assimilation, and are run at very high resolutions. The monsoon depression is well resolved by these respective horizontal resolutions in these models (at 15 and 35km). These models carry complete and detailed physical parameterizations. The false alarms in their forecasts leads us to suggest that some additional important ingredient may be missing in these current best state of the art models. This paper addresses the effects of pollution for the enhancement of cloud condensation nuclei and the resulting disruption of the organization of convection in monsoon depressions. Our specific studies make use of a high resolution mesoscale model (WRF/CHEM) to explore the impacts of the first and second aerosol indirect effects proposed by Twomey and Albrecht. We have conducted preliminary studies including examination of the evolution of radar reflectivity (computed inversely from the model hydrometeors) for normal and enhanced CCN effects (arising from enhanced monsoon pollution). The time lapse histories show a major disruption in the organization of convection of the monsoon depressions on the time scale of a week to ten days in these enhanced CCN scenarios.

Proceedings Article | 16 November 2010 Paper

Downscaled multi-model superensemble and probabilistic forecasts of seasonal rains over the Asian monsoon belt

T. Krishnamurti, Vinay Kumar

Proceedings Volume 7856, 78560G (2010) https://doi.org/10.1117/12.871078

KEYWORDS: Climatology, Data modeling, Meteorology, Atmospheric modeling, Statistical modeling, Atmospheric sciences, Data analysis, Data acquisition, Error analysis, Performance modeling

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This is a study on seasonal climate forecasts for the Asian Monsoon region. The unique aspect of this study is that it became possible to use the forecast results from as many as 16 state of the art coupled atmosphere-ocean models. A downscaling component, with respect to observed rainfall estimates uses data sets from TRMM and a dense rain gauge distriburion; this enables the forecasts of each model to be bias corrected to a common 25 km resolution. The downscaling statistics for each model, at each grid location is developed during a training phase of the model forecasts; the forecasts from all of the member models use the downscaling coefficients of the training phase. These forecasts are next used for the construction of a multimodel superensemble. A major result of this paper is on the climatology of the model rainfall. From the downscaled multimodel superensemble which shows a correlation of nearly 1.0 with respect to the observed climatology. This high skill is important for addressing the rainfall anomaly forecasts, which are defined in terms of departures from the observed (rather than a model based) climatology. The second part of this study addresses seasonal climate forecasts of Asian monsoon precipitation anomalies. Seasonal climate forecasts over the larger monsoon Asia domain and over the regional belts are evaluated. The superensemble forecasts invariably carry the highest skill compared to the member models globally and regionally. This relates largely to the presence of large systematic errors in models that carry low seasonal prediction skills. Such models carry persistent signatures of systematic errors, and their errors are recognized by the multimodel superensemble. One of the conclusions of this study is that given the uncertainties in current modeling for seasonal rainfall forecasts, post processing of multimodel forecasts, using the superensemble methodology, seems to provide the most promising results for the rainfall anomaly forecasts.

Proceedings Article | 3 December 2008 Paper

Adjustment of TRMM rainfall algorithm for monsoon land areas

Anu Simon, A. K. Mishra, T. N. Krishnamurti

Proceedings Volume 7148, 71480H (2008) https://doi.org/10.1117/12.806428

KEYWORDS: Meteorology, Microwave radiation, Ions, Data archive systems, Calibration, Data modeling, Statistical modeling, Climatology, Infrared radiation, Temporal resolution

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Proceedings Article | 3 December 2008 Paper

Impact of Arabian Sea pollutions on the Bay of Bengal winter monsoon rains

T. N. Krishnamurti, Arindam Chakraborty, Andrew Martin, William Lau, Yogesh Sud, Gregory Walker

Proceedings Volume 7148, 71480D (2008) https://doi.org/10.1117/12.806393

KEYWORDS: Aerosols, Pollution, Meteorology, MODIS, Atmospheric modeling, Climatology, Atmospheric particles, Troposphere, Clouds, Systems modeling

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Proceedings Article | 11 December 2006 Paper

Assimilation and seasonal forecasts of SST and upper ocean temperatures using satellite and ARGO data base

T. Krishnamurti, Arindam Chakraborty

Proceedings Volume 6404, 640407 (2006) https://doi.org/10.1117/12.697075

KEYWORDS: Atmospheric propagation, Wave propagation, Satellites, Atmospheric modeling, Data modeling, Meteorological satellites, Meteorology, Earth observing sensors, Clouds, Profiling

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Proceedings Article | 8 December 2006 Paper

Prediction of the diurnal cycle of clouds using a multimodel superensemble and ISCCP data sets

Arindam Chakraborty, T. Krishnamurti, C. Gnanaseelan

Proceedings Volume 6404, 64040A (2006) https://doi.org/10.1117/12.697088

KEYWORDS: Clouds, Climatology, Atmospheric modeling, Global system for mobile communications, Spectral models, Earth's atmosphere, Statistical modeling, Meteorology, Environmental sensing, Remote sensing

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