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  1. AU="Raju Mandal"
  2. AU="Owen, Noel L"
  3. AU=Liu Xiaolei
  4. AU="Fırıncıoğluları, Ali"
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  1. Article ; Online: Multi-Model Multi-Physics Ensemble

    Atul K. Sahai / Manpreet Kaur / Susmitha Joseph / Avijit Dey / R. Phani / Raju Mandal / Rajib Chattopadhyay

    Frontiers in Climate, Vol

    A Futuristic Way to Extended Range Prediction System

    2021  Volume 3

    Abstract: In an endeavor to design better forecasting tools for real-time prediction, the present work highlights the strength of the multi-model multi-physics ensemble over its operational predecessor version. The exiting operational extended range prediction ... ...

    Abstract In an endeavor to design better forecasting tools for real-time prediction, the present work highlights the strength of the multi-model multi-physics ensemble over its operational predecessor version. The exiting operational extended range prediction system (ERPv1) combines the coupled, and its bias-corrected sea-surface temperature forced atmospheric model running at two resolutions with perturbed initial condition ensemble. This system had accomplished important goals on the sub-seasonal scale skillful forecast; however, the skill of the system is limited only up to 2 weeks. The next version of this ERP system is seamless in resolution and based on a multi-physics multi-model ensemble (MPMME). Similar to the earlier version, this system includes coupled climate forecast system version 2 (CFSv2) and atmospheric global forecast system forced with real-time bias-corrected sea-surface temperature from CFSv2. In the newer version, model integrations are performed six times in a month for real-time prediction, selecting the combination of convective and microphysics parameterization schemes. Additionally, more than 15 years hindcast are also generated for these initial conditions. The preliminary results from this system demonstrate appreciable improvements over its predecessor in predicting the large-scale low variability signal and weekly mean rainfall up to 3 weeks lead. The subdivision-wise skill analysis shows that MPMME performs better, especially in the northwest and central parts of India.
    Keywords multi-physics ; multi-model ; extended range prediction ; monsoon ; ensemble prediction ; Environmental sciences ; GE1-350
    Subject code 612
    Language English
    Publishing date 2021-05-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Real time extended range prediction of heat waves over India

    Raju Mandal / Susmitha Joseph / A. K. Sahai / R. Phani / A. Dey / R. Chattopadhyay / D. R. Pattanaik

    Scientific Reports, Vol 9, Iss 1, Pp 1-

    2019  Volume 11

    Abstract: Abstract Heat waves over India occur during the months of March-June. This study aims at the real-time monitoring and prediction of heat waves using a multi-model dynamical ensemble prediction system developed at Indian Institute of Tropical Meteorology, ...

    Abstract Abstract Heat waves over India occur during the months of March-June. This study aims at the real-time monitoring and prediction of heat waves using a multi-model dynamical ensemble prediction system developed at Indian Institute of Tropical Meteorology, India. For this, a criterion has been proposed based on the observed daily gridded maximum temperature (Tmax) datasets, which can be used for real-time prediction as well. A heat wave day is identified when either (1) Tmax (a)≥ its climatological 95th percentile (calculated from daily values during March-June and for 1981–2010), (b) >36 °C, and (c) its departure from normal is >3.5 °C, Or, (2) when the Tmax >44 °C. Three heat wave prone regions, namely, northwest, southeast and northwest-southeast regions are recognized and heat wave spells of minimum consecutive six days are identified objectively for each region during 1981–2018. It is noticed that the prediction system has reasonable skill in predicting the heat waves over heat wave prone regions of India. Forecast verification of heat wave spells during 2003–2018 reveals that the prediction system has great potential in providing overall indication about the onset, duration and demise of the forthcoming heat wave spell with sufficient lead time albeit with some spatio-temporal error.
    Keywords Medicine ; R ; Science ; Q
    Subject code 551
    Language English
    Publishing date 2019-06-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Development of a probabilistic early health warning system based on meteorological parameters

    A. K. Sahai / Raju Mandal / Susmitha Joseph / Shubhayu Saha / Pradip Awate / Somenath Dutta / Avijit Dey / Rajib Chattopadhyay / R. Phani / D. R. Pattanaik / Sunil Despande

    Scientific Reports, Vol 10, Iss 1, Pp 1-

    2020  Volume 13

    Abstract: Abstract Among the other diseases, malaria and diarrhoea have a large disease burden in India, especially among children. Changes in rainfall and temperature patterns likely play a major role in the increased incidence of these diseases across ... ...

    Abstract Abstract Among the other diseases, malaria and diarrhoea have a large disease burden in India, especially among children. Changes in rainfall and temperature patterns likely play a major role in the increased incidence of these diseases across geographical locations. This study proposes a method for probabilistic forecasting of the disease incidences in extended range time scale (2–3 weeks in advance) over India based on an unsupervised pattern recognition technique that uses meteorological parameters as inputs and which can be applied to any geographical location over India. To verify the robustness of this newly developed early warning system, detailed analysis has been made in the incidence of malaria and diarrhoea over two districts of the State of Maharashtra. It is found that the increased probabilities of high (less) rainfall, high (low) minimum temperature and low (moderate) maximum temperature are more (less) conducive for both diseases over these locations, but have different thresholds. With the categorical probabilistic forecasts of disease incidences, this early health warning system is found to be a useful tool with reasonable skill to provide the climate-health outlook about possible disease incidence at least 2 weeks in advance for any location or grid over India.
    Keywords Medicine ; R ; Science ; Q
    Language English
    Publishing date 2020-09-01T00:00:00Z
    Publisher Nature Publishing Group
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

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