ACROSS

Monsoon Convection, Clouds, and Climate Change (MC4)

Clouds are an integral part of monsoon convection and precipitation. Current understanding of the coupling of monsoon dynamics to convection and cloud processes is limited due to the lack of observations and inadequate representation of cloud processes in climate models.

The MC4 scheme was envisioned to improve the observational database and climate models for enhanced predictive understanding of monsoonal precipitation changes and their impacts in a warming environment. The overarching goal of MC4 is to describe better and quantify interactions among monsoon dynamics, clouds, aerosols, precipitation and water cycle in a changing climate. It will be accomplished by climate modelling and observational studies and would enable improved prediction of climatic variations and regional impacts over south Asia.

Objectives of MC4

1. Modelling of global and regional climate variability and change, and assess impact using Earth System Models and high-resolution climate models.
2. Conduct observational programs, numerical simulations and laboratory investigations focusing on monsoon cloud dynamics and microphysics.
3. Conduct ground-based measurements of greenhouse gas (GHG) concentrations and fluxes, chemical trace gases and meteorological parameters.
4. Reconstruct past variations in the Asian monsoon extending back to several thousands of years.
5. Outreach, training and dissemination of reliable climate information.
6. Build in-house capacity in global and regional climate modelling and observations.
7. Establish an Atmospheric Research Testbed in Central India (ART-CI).
8. Establish a National Climate Reference Network (NCRN).

 

To achieve these objectives, MC4 has the following four sub-programmes.

1. Centre For Climate Change Research (CCCR) including virtual water centre

A state-of-the-art CCCR was established at the Indian Institute of Tropical Meteorology (IITM), Pune, to improve the understanding of climate change in the tropics and to enable improved assessments of regional climate responses to global climate change.

Achievements of CCCR

1. Contributed to CMIP6 (Coupled Model Intercomparison Project) experiments and IPCC (Intergovernmental Panel on Climate Change) sixth assessment report (AR6), for the first time from India, using the IITM Earth System Model (IITM-ESM). Multi-century simulations corresponding to pre-industrial and present-day conditions on India’s first Earth System Model version-2 (IITM-ESMv2) show significant improvements in capturing key aspects of the time-mean atmosphere and ocean large scale circulation.
2. Completed several DECK (Diagnostic, Evaluation, and Characterisation of Klima) simulations of CMIP6. These include simulations of 300-year spin-up and 500-year pre-industrial control, recent historical past (~150 years), AMIP (Atmospheric Model Intercomparison Project), and transient carbon dioxide (CO2) and abrupt CO2 increase.
3. Generated an ensemble of high resolution downscaled projections of regional climate until 2100 for the IPCC scenarios (Representative Concentration Pathway: RCP4.5 and RCP8.5) at CCCR, IITM using a regional climate model (International Centre for Theoretical Physics: ICTP-RegCM4) at a 50 kilometres (km) resolution as part of the Coordinated Regional Downscaling Experiment (CORDEX) South Asia activity.
4. Developed and tested a 27 km global model of the atmospheric-only component of the IITM-ESM for downscaling studies in the coming years (2020-24).

2. Physics and Dynamics of TropicalClouds (PDTC)

PDTC aims to advance the understanding of tropical clouds and their interaction with the environment. Thisis required for better prediction of monsoon, and to establish a scientific rationale for cloud seeding operations to increase rainfall efficiency. It has the following four sub-projects.

1. Cloud and Aerosol Interaction and Precipitation Enhancement EXperiment (CAIPEEX)
2. High Altitude Cloud Physics Laboratory (HACPL) in Maharashtra
3. Thunderstorm dynamics
4. Radar and satellite meteorology

Achievements of PDTC

1. Implemented a three years cloud seeding research program with physical and statistical evaluation. A total of 234 randomised samples were collected for formulating protocols for cloud seeding.
2. Conducted airborne observations of seeded and unseeded clouds with their microphysical changes.
3. Established an observational facility in the rain shadow region of Western Ghats for clouds, precipitation for weather modification research. It was done with the following four facilities.
   1. Laboratory with c-band radar, microwave radiometer, wind profiler, ceilometer, and radiosonde for cloud studies, aerosol, cloud condensation nuclei (CCN), ice nucleating particle (INP) boundary layer flux measurements, etc.
   2. Rain gauge network of over 120 rain gauges in Solapur, Maharashtra for seeding evaluation and rainfall climatology over the region.
   3. Dynamical and thermodynamic measurements of boundary layer and cloud layer in colocation with c-band radar at Tuljapur, Maharashtra.
   4. Numerical modeling for cloud and precipitation assessment to assist decision making and evaluation for operational seeding.
4. Established a state-of-art lightning location network over India with about 75 sensors installed in several states. All sensors are integrated with the central processor at IITM. The network will be further expanded with a few more sensors to be added in the future.
5. Augmented measurements of aerosol chemistry, aerosol hygroscopicity, and volatile organic compounds (VOC) to improve the understanding of CCN activation process.
6. Conducted INP experiments to understand the variability at mixed-phase cloud conditions. Data sets are being used to develop ice nucleation parameterisation scheme and test.
7. Continued measurements of aerosol, cloud and precipitation to understand their interactions and orographic precipitation processes.
8. Established radar facilities in Mandhardev, Maharashtra (precipitation X-band since 2012 and cloud Ka-band since 2014] to understand the processes of orographic cloud and precipitation.
9. Used ice nucleation measurements to develop ice microphysics parameterisation and testing in the weather forecast model.
10. Established an Atmospheric Electricity Observatory (AEO) at Bharati, Indian station at Antarctica and SGU University Kolhapur, Maharashtra.
11. Measured aerosol, radiation, precipitation and cloud microphysics at HACPL, Mahabaleshwar, Maharashtra continuously since 2012.
12. Implemented measurements of VOC using a high-sensitivity proton transfer reaction mass spectrometer at HACPL to understand their role on secondary organic aerosols, and activation processes of CCN and ice nuclei.
13. Augmented hygroscopicity measurements for CCN closure studies and CCN parameterisation.
14. Provided technical guidance to the states on cloud seeding.
15. Established a Fluid Dynamics Laboratory with particle image velocimetry (PIV) systems and hot wire anemometry to study wall jets and shear flows.
16. Developed a mobile application named ‘Damini-Lightning Alert’.
17. Developed a mobile application-based meteorological dissemination system for Kumbh Mela.
18. Established a rain gauge network in Mumbai metropolitan region, and developed web-based data portal and mobile application to disseminate information for developing flood warning and forecast validation experiments to the public and MoES institutes.
19. Set up a MESO network of 40 ARGs in Mumbai metropolitan region.
20. Collated rainfall data from India Meteorological Department (IMD) and Municipal Corporation of Greater Mumbai (MCGM) into a web portal and mobile application.
21. Provided data of rainfall from ~100 stations to National Center for Coastal Research (NCCR), Chennai and National Center for Medium Range Weather Forecasting (NCMRWF), Noida for developing flood warning system and for forecast validation experiments.
22. Carried out winter fog field campaign at Indira Gandhi International (IGI) Airport, New Delhi in successive winter seasons. This was done to describe the environmental conditions in which fog develops, by measuring surface micrometeorology, radiation balance, turbulence, the thermodynamic structure of the surface layer, fog droplet, and aerosol microphysics fog water chemistry.
23. Established an observational facility for fog research at, IGI airport Delhi.
24. Developed an operational fog forecasting system at IGI airport, New Delhi.
25. Acquired 100 acres of land in Sehore from the government of Madhya Pradesh for establishment of Atmospheric Research Testbed in the core monsoon zone. Infrastructure development of ARThas commenced.

3. Atmospheric Research Testbeds (ART) for process studies and National Climate Reference Network (NCRN)

The ART program is a highly focused observational and analytical research effort that will use collocated observations from advanced measurement systems to understand various atmospheric processes, particularly cloud, land-atmosphere interactions and radiative processes, testing parameterisations of these processes for use in atmospheric models.

In the first phase, an ART would be set up in central India to study convection, land-atmosphere interactions and precipitation processes. It is expected to provide a sound basis for other research testbed programs in climatologically interesting and important areas. In the second phase, ART is to be set up in the northeast/eastern part of the country to study severe thunderstorm processes.

The NCRN will comprise of a network of ~25 climate reference stations commissioned in several parts of the country, equipped to provide long-term, accurate, and unbiased observations. Such observations are essential to define the state of the integrated Earth system, its history, and its future variability and change. Most historical in situ observations of surface climate variables have been undertaken for real-time applications such as weather forecasting, hydrology, and agro-meteorology. Changes during the time of observation, instrumentation, and operators also contribute to uncertainty. Historically, undocumented or inadequate metadata that describes changes in meteorological measurements has been all too common. The NCRN will make it possible to validate the climate projections from ESMs using accurate and reliable ground truth verifications.

Achievements of ART and NCRN

1. Acquired 100 acres of land in Silkheda, TahashilShyampur, Sehore, Madhya Pradesh (a core monsoon zone) to establish an ART. Planning and development of physical and equipment infrastructure are under progress.
2. Site selection for setting up climate reference stations is under progress.

4. Metro Air Quality and Weather Service (MAQWS)

MAQWS is a nearly warning system of air quality of Delhi. It was launched on 15th October 2018 and was developed in collaboration with the National Center for Atmospheric Research (NCAR), USA. The system assimilates data from ~36 monitoring stations which are run by the Central Pollution Control Board (CPCB), Delhi Pollution Control Committee (DPCC), and the System of Air Quality and Weather Forecasting and Research (SAFAR). The system provides location-specific information on air quality in near real-time and its forecast with a lead time of 1 to 3 days. Data from satellites on stubble burning in northwest India or dust storms along with the prevalent meteorological factors helps to improve the initial conditions of the dynamical chemistry transport model. This enables accurate prediction of air-quality, which aids in planning of mitigation strategies. The scientific outcome of MAQWS will allow the implementation of a graded response action plan well in advance. Such an approach could lead to more targeted and cost-effective action on weather information and clean air, focused on public health.

Achievements

1. Developed an advance version of Fine Dust Model with various dust schemes for large-scale dust storm and transport pathways to predict extreme events in northwest India including Delhi. Augmented it with SAFAR.
2. The first indigenous impact study of the impact of air pollution on health to understand exposure to particulate matter pollution and their effects on disease burden and life expectancy in each state of India.
3. Developed a first very high-resolution emission inventory of Delhi for 2018 and identified relative share of various sources.
4. Developed a dynamic gridded emission inventory of Kharif agriculture residue burning of north Indian states of Punjab and Haryana by synergising three satellite datasets (INSAT-3D, INSAT-3DR and MODIS data) with ground-real measurements. Quantified the contribution of stubble burning in Punjab and Haryana to the air quality of Delhi.
5. Developed a mechanism to understand prolonged air pollution emergency of Delhi in 2017 and its empirical relationship with fog.
6. Released advanced version of SAFAR-Air mobile application with multilingual and voice-enabled features for air quality forecast.

High Performance Computing System (HPCS)

MoES is mandated to provide the nation with the best possible services of forecasting the monsoons and other weather and climate parameters, ocean state, natural disasters such as earthquakes and tsunamis, and other phenomena related to earth systemnation. Improving these forecasts is a challenging task as it requires solving of complex mathematical equations at a very high spatial resolution across the entire globe. The models to solve these equations need high-performance computational resources, including modern supercomputers with vast parallel computing architecture with support from artificial intelligence and machine learning algorithms.

The existing HPCS resources of 6.8 petaflops (PF) commissioned in 2018 has resulted in improved short-medium scale forecasts with the usage of high-resolution models. For further enhancing weather and climate prediction, high-resolution dynamical models with increased complexity and advanced data assimilation techniques are required, which are highly computationally intensive. Rigorous developmental work has been undertaken at MoES institutes which include following important tasks.

MoES has taken up work to develop the following essential initiatives as part of the HPCS programme.

1. Enhance model resolution for weather and climate.
2. Improve forecasts in the short, medium and long-range scales for monsoon mission programs that involve sensitivity experiments for various physical processes.
3. Develop ensemble prediction models with more members.
4. Employ numerical techniques and atmosphere-ocean coupled model along with probabilistic forecasts with quantified uncertainty.

Adequate computational facilities are also required to enhance training capacity to cater to the enormous need for skilled manpower in the field of Earth System sciences. In addition, real-time weather and climate-related information and services are provided to the SAARC (South Asian Association for Regional Cooperation), IOR-ARC (Indian Ocean Rim Association for Regional Co-operation), RIMES (Regional Integrated Multi-hazard Early Warning System), ASEAN (Association of Southeast Asian Nations) countries for societal benefit.

MoES also hosts and established the Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) Centre for Weather and Climate, the Indo Africa Centre for Medium Range Weather Prediction in Mauritius, and the National Tsunami Early Warning Centre (which has been providing tsunami advisory services to India and IOR-ARC countries). High computational power is required to carry out all activities related to the flagship programs of MoES.

Objectives

1. Provide HPCS facility and extend support to neighbouring BIMSTEC countries, Indo-Africa Centre and other African countries.
2. Make computational resources available to academic and R&D community to improve forecast skill and work on the operational forecasting system.
3. Establish a comprehensive computational and visualization ecosystem to deliver state-of-the-art big-data analytics and artificial intelligence/machine learning framework.
4. Maintain HPCS facility by providing necessary infrastructures and support such as uninterruptible power source (UPS), cooling system, power and generator backup.

Implementing institutions

1. Indian Institute of Tropical Meteorology (IITM), Pune.
2. National Centre for Medium Range Weather Forecast (NCMRWF), Noida.

Achievements

1. The computational capacity of the HPCS was enhanced to ~1.3 PF in phase I (2013-14) by an additional 1.1 PF. The facility is established at IITM and NCMRWF with access to other units of MoES through dedicated NKN linkage.
2. HPC system of 4.0 PF was installed at IITM and of 2.8 PF was installed at NCMRWF in phase 2. The storage capacity of this system is 8 PB (petabyte) at IITM and 5.6 PB at NCMRWF.
3. The HPC system at NCMRWF is for operational and research activities of IMD, NCMRWF and Indian National Center for Ocean Information Services (INCOIS), Hyderabad.
4. The HPC system at IITM is mainly for research and operational runs of IMD for seasonal and extended range predictions and air quality early warning systems. It is also used to conduct Intergovernmental Panel on Climate Change (IPCC) climate projections and other historical and natural runs. A total of 16.7 PB (9.7 PB at IITM and 7.0 PB at NCMRWF) disk storage has been installed.
5. A total of 36 PB (27 PB at IITM and 19 PB at NCMRWF) tape library is available for storing historic data.
6. A 5 PB HPS Server has been deployed on pilot basis for long term data archival requirements at IITM and NCRMRWF.
7. HPC systems at NCMRWF and IITM were maintained at more than 99% uptime.
8. A mirror site was created at IITM for operational runs of INCOIS, IMD and NCMRWF as measures to mitigate un/planned shutdown.
9. Atmospheric Research Data Centre was maintained for various stakeholders, including scientists and citizens.

Plans

1. Augment HPC resources with at least 40 PF by 2022, 150 PF by 2025 and 500 PF by 2030.
2. Support research projects on parallelization and scalability of HPC resources and data centre design and practices.

 

MONSOON MISSION (MM-II)

Agricultural productivity and economy of our country largely depends on the performance of Indian monsoon rainfall. Therefore, prediction of total quantum of Indian summer monsoon rainfall (ISMR) during the months of June to September (also called the seasonal rainfall, which produces about 80% annual rainfall over the country), its intra-seasonal and inter-annual variability, and knowledge of extreme rainfall conditions are very useful for planning and managing agriculture, water resources and disaster management, leading to great benefit to the society and citizens of the country. The ISMR has a global teleconnection with El Nino, which relates to an anomalous warming of Sea Surface Temperature (SST) over East Pacific Ocean and its opposite phase La-Nina, relating to cooling of SST over the same region. This has a predictive value for seasonal prediction of ISMR, as the signal is obtained few months in advance.

In last few decades, many studies have been made on the El Nino and Southern Oscillation (ENSO) phenomenon which is a dominant mode of global inter-annual variability with vast influence on other regional climates. However, till a decade ago (up to 2010), no significant breakthrough had taken place in improving the prediction skill of the ISMR. Historically, statistical models had been used for operational long-range forecasts for the Indian summer monsoon rainfall over the years. But improvement in prediction skill was not appreciable in operational forecasts, in spite of better understanding of monsoon variability, its teleconnection mechanisms and the knowledge that it is a prominent heat source over Indian region that drives the major atmospheric circulations. Moreover, statistical models had constraints in predicting monsoon rainfall in higher spatial and temporal resolutions.

Recent improvements in dynamical numerical models with ocean-atmosphere coupling have shown good prediction skill of ENSO SST with six months lead time. The seasonal mean rainfall hind cast skill, at one season lead time, over the central Pacific is also very good. In recent times, with the dynamical models, several new approaches (high resolution, improved physical parameterization schemes, super parameterizations, data assimilation, etc.) have shown that the variability in tropics can be reasonably resolved, thereby creating optimism for improving the monsoon prediction. Although many centres in the world were using dynamical modelling frameworks to predict seasonal mean climate routinely, in India such a framework was not in place before 2012.

The Ministry of Earth Sciences (MoES), Government of India, launched the National Monsoon Mission (NMM) in 2012 (now referred as Monsoon Mission, MM), with a vision to develop a state-of-the-art dynamical prediction system for Indian monsoon rainfall on different time scales. MoES bestowed the responsibility of execution and coordination of this mission to the Indian Institute of Tropical Meteorology (IITM), Pune in collaboration with the National Centre for Environmental Prediction (NCEP), USA, other MoES organizations (NCMRWF, IMD & INCOIS) and various national and international academic institutions and organizations. Climate Forecast System (CFS) of NCEP was found to be one of the best among the currently available coupled climate models, and its second version (CFSv2) has been implemented at IITM Pune, as the basic modelling system for the above purpose. Scientists of IITM, along with collaborators, made necessary model development works on this base model for improving prediction skill of this model over Indian monsoon region, with decreased model bias. Unified Model (UM) of UK Meteorological Office was implemented at NCMRWF, Noida as the base model for short and medium range weather predictions. NCMRWF scientists and collaborators worked on this model. In addition, CFS & GFS based models were used for extended range prediction and high-resolution short-range prediction at IITM Pune, in collaboration with IMD and NCMRWF. Model data assimilation works were done at NCMRWF, INCOIS and IITM, in collaboration with the University of Maryland, USA. High Performance Super Computing Systems (HPCS), installed at IITM and NCMRWF, provided the modelling infrastructure. Several national and international projects were funded through MM and those were coordinated by the Monsoon Mission Directorate (MMD) at IITM, with guidance of important Committees formed by MoES. There had been many high-level training courses, manpower development works, deputation of young scientists abroad for working with international principal investigators, high-level meetings and events during the MM-I.

In 2017, the first phase of monsoon mission (referred to as MM-I) was completed successfully. The seasonal prediction system with improved hind cast skill (retrospective forecast of seasonal monsoon) was handed over to IMD for operational forecasting and this modified model is referred as Monsoon Mission CFS (MMCFS). The extended range prediction system was also handed over to IMD for operational forecasting of active/break spells of monsoon and other weather events, up to 4 weeks in advance. The success of MM-I led to its continuance, as the second phase.

The second phase of monsoon mission (MM-II), which began in September 2017, focuses on predicting weather/climate extremes and development of climatic applications based on monsoon forecasts, especially in the field of agriculture, hydrology and energy sector, while continuing model development activities. In MM-II, focus has been given to high-resolution short-range predictions, predicting extremes, and using forecasts to develop applications for agriculture, hydrology, disaster management, energy sector, etc. As a new initiative to predict extremes, dynamical prediction of thunderstorm and lightning has been initiated. Model development, through enhancement in resolution and improvement in physical processes in the model, is continuing for increasing prediction skill of Indian monsoon and minimizing model biases.

Objectives

1. To build a working partnership between the academic and R&D organizations, both national and international, to improve the operational monsoon forecast skill over the country.
2. To setup a state-of-the-art dynamical modelling frame work for improving prediction skill of ‘seasonal and extended range predictions’ and ‘short and medium range (up to two weeks) prediction’.

Implementing institutions

1. Indian Institute of Tropical Meteorology (IITM), Pune
2. National Centre for Medium Range Weather Forecast (NCMRWF), Noida
3. India Meteorological Department (IMD), New Delhi
4. Indian National Centre for Ocean Information Services (INCOIS), Hyderabad

Achievements

1. Short-range prediction: The world’s highest resolution Global Ensemble Forecast System (GEFS) for short range prediction at 12 km using 21 members of the model was developed by IITM and handed over to IMD for operationalization. The 12.5 km EPS based rainfall probability has enabled to initiate block level forecast of rainfall probability for IMD’s agromet application. The model has improved forecast of heavy rain and tropical cyclone track, intensity and landfall. The high-resolution forecast has also helped in forest fire outlook based on model prediction of soil moisture, rainfall and wind forecast.
2. Data Assimilation: Coupled ocean-atmospheric data assimilation system using Local Ensemble Transform Kalman Filter (LETKF) technique (weakly coupled) has been developed and implemented for CFSv2 at Aaditya HPC, IITM. Land data assimilation work has started at IITM recently.
3. Seamless Prediction system: Initiated a seamless prediction system version 0.0 by coupling MoM5 to the existing Monsoon Mission model.
4. Seasonal Prediction: During 2019-2020, the operational seasonal forecast of south west monsoon was prepared using Monsoon Mission Climate Forecast System (MMCFS), developed at IITM, Pune. Model development for climatic application in hydrology and agriculture by integrating river runoff in CFSv2 is in progress.
5. Extended Range Prediction: Developed strategy for the real-time extended range prediction of heat waves, a methodology to predict the Madden-Julian Oscillation in real-time on extended range, and improved genesis potential parameter to predict cyclogenesis in real-time.
6. Thunderstorm and lightning prediction system: New initiatives were taken up to develop a modelling framework for thunderstorm and lightning prediction using dynamical lightning parameterization (DLP) in WRF model.
7. Short-range high-resolution ensemble forecasting: Developed and operationalized percentile based (90th and 95th) extreme forecast of rainfall based on GFS (12 km) model, developed a probabilistic forecast for all the river basins of India based on GEFS ensemble forecast and operationalized by IMD’s flood monitoring offices (FMOs) for various river basins of India. GEFS based indices namely supercell composite parameter (SCP), wind gust index, and hail index have been developed for prediction of thunderstorm occurrence, wind gust, and hail.

Plans

1. Making very high-resolution forecasts by developing models to treat sub-grid scale phenomenon better than the present day models. (Approach should be observations → DNS → LES→GCM)
2. Developing tools (using artificial intelligence and machine learning algorithms) for generating 1-km weather forecasts and to enhance the skill of dynamical models at extended and seasonal time scales.
3. Incorporating new modules (wave model, ocean bio-geo chemistry, chemistry-aerosols etc.) in the climate models so that advisories of various products like potential fishing zones, ocean state forecasts, and other activities that can be initiated at seasonal and extended range time scales.

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Atmospheric, Climate Science and Services

ACROSS is composed of the following four sub-schemes.

1. National Facility for Airborne Research (NFAR)
2. Monsoon Convection, Clouds, and Climate Change (MC4)
3. High Performance Computing System (HPCS)
4. Monsoon Mission (MM-II)

Details of the sub-schemes are provided below.

National Facility for Airborne Research (NFAR)

In the present climatic scenario, a need for long term airborne measurements is strongly felt by the scientific community in the country. This will help to improve understanding of the atmospheric processes in the monsoon environment in particular, and cloud-aerosol-radiative feedback mechanisms that impact climate variability and change over India in times to come.

Cloud microphysical parameters show large variability due tovarious factors such asaerosol number concentrations, turbulence, mixing of environmental dry air, andother large-scale conditions.The numerical high-resolution regional cloud models need characteristic values of these parameters for parameterizations of various empirical processes, validation and prediction purposes. MoES is in the process of procuring an Instrumented Aircraft System as a National Facility for Airborne Research (NFAR).It will be equipped with state-of-the-art instruments to measure different atmospheric parameters of meteorology, cloud physics, aerosols and air chemistry. The airborne platform is projected as a national facility that will cater to the scientific needs of several national research and educational institutions in the country.

IAS will be managed by the Indian Institute of Tropical Meteorology (IITM), Pune—an institute under the MoES.Facilities for maintenance repair, installation, calibration and modifications of scientific instruments on IAF will be made available at the hangar planned at Aurangabad airport in Maharashtra.It will be operated from various bases across the country, depending on the research objective.

Objectives

1. Procurement of an IASunder the NFAR.
2. Facilitate seasonal cloud microphysics and aerosol observations using the instrumented airborne platform for studying cloud aerosol interactions across the country.
3. Facilitate observations for understanding the interactions between clouds and large-scale environment which would be used to develop physical parameterization schemes useful in numerical models used for monsoon prediction.
4. Address air pollution and associated impacts (health, visibility, climate), environmental and hydrological studies.
5. Develop UncrewedAerial Vehicles (UAV) with state-of-the art-instrumentation for lower atmospheric research and boundary layer process studies as institutional (IITM) activity.

Achievements

1. MoES is considering the establishmentof NFAR by procuring an IAS.
2. UAV systems with meteorological and atmospheric instrumentation for lower atmospheric research.
3. A laboratory named as ‘Lower Atmospheric Research using Unmanned Aerial System Facility (LARUS)’ is established at IITM with necessary infrastructure.

Plans

1. Procurement process of an IAS.
2. Construction of a hangar with infrastructure and laboratory facilities.
3. Design experiments with UAV systems and conduct research flights and missions for profiling the lower atmosphere for process studies, models (with permission from the Directorate General of Civil Aviation).
4. Obtain new generation sophisticated UAV platforms and state-of-the-art atmospheric sensors for targeted observations.
5. Obtain a mobile Ground Control Station for UAV field operation.

 

Monsoon Convection, Clouds, and Climate Change (MC4)

Clouds are an integral part of monsoon convection and precipitation. Current understanding of the coupling of monsoon dynamics to convection and cloud processes is limited due to the lack of observations and inadequate representation of cloud processes in climate models.

The MC4 scheme was envisioned to improve the observational database and climate models for enhanced predictive understanding of monsoonal precipitation changes and their impacts in a warming environment. The overarching goal of MC4 is to describe better and quantify interactions among monsoon dynamics, clouds, aerosols, precipitation and water cycle in a changing climate. It will be accomplished by climate modelling and observational studies and would enable improved prediction of climatic variations and regional impacts over south Asia.

Objectives of MC4

1. Modelling of global and regional climate variability and change, and assess impact using Earth System Models and high-resolution climate models.
2. Conduct observational programs, numerical simulations and laboratory investigations focusing on monsoon cloud dynamics and microphysics.
3. Conduct ground-based measurements of greenhouse gas (GHG) concentrations and fluxes, chemical trace gases and meteorological parameters.
4. Reconstruct past variations in the Asian monsoon extending back to several thousands of years.
5. Outreach, training and dissemination of reliable climate information.
6. Build in-house capacity in global and regional climate modelling and observations.
7. Establish an Atmospheric Research Testbed in Central India (ART-CI).
8. Establish a National Climate Reference Network (NCRN).

 

To achieve these objectives, MC4 has the following four sub-programmes.

1. Centre For Climate Change Research (CCCR) including virtual water centre

A state-of-the-art CCCR was established at the Indian Institute of Tropical Meteorology (IITM), Pune, to improve the understanding of climate change in the tropics and to enable improved assessments of regional climate responses to global climate change.

Achievements of CCCR

1. Contributed to CMIP6 (Coupled Model Intercomparison Project) experiments and IPCC (Intergovernmental Panel on Climate Change) sixth assessment report (AR6), for the first time from India, using the IITM Earth System Model (IITM-ESM). Multi-century simulations corresponding to pre-industrial and present-day conditions on India’s first Earth System Model version-2 (IITM-ESMv2) show significant improvements in capturing key aspects of the time-mean atmosphere and ocean large scale circulation.
2. Completed several DECK (Diagnostic, Evaluation, and Characterisation of Klima) simulations of CMIP6. These include simulations of 300-year spin-up and 500-year pre-industrial control, recent historical past (~150 years), AMIP (Atmospheric Model Intercomparison Project), and transient carbon dioxide (CO2) and abrupt CO2 increase.
3. Generated an ensemble of high resolution downscaled projections of regional climate until 2100 for the IPCC scenarios (Representative Concentration Pathway: RCP4.5 and RCP8.5) at CCCR, IITM using a regional climate model (International Centre for Theoretical Physics: ICTP-RegCM4) at a 50 kilometres (km) resolution as part of the Coordinated Regional Downscaling Experiment (CORDEX) South Asia activity.
4. Developed and tested a 27 km global model of the atmospheric-only component of the IITM-ESM for downscaling studies in the coming years (2020-24).

2. Physics and Dynamics of TropicalClouds (PDTC)

PDTC aims to advance the understanding of tropical clouds and their interaction with the environment. Thisis required for better prediction of monsoon, and to establish a scientific rationale for cloud seeding operations to increase rainfall efficiency. It has the following four sub-projects.

1. Cloud and Aerosol Interaction and Precipitation Enhancement EXperiment (CAIPEEX)
2. High Altitude Cloud Physics Laboratory (HACPL) in Maharashtra
3. Thunderstorm dynamics
4. Radar and satellite meteorology

Achievements of PDTC

1. Implemented a three years cloud seeding research program with physical and statistical evaluation. A total of 234 randomised samples were collected for formulating protocols for cloud seeding.
2. Conducted airborne observations of seeded and unseeded clouds with their microphysical changes.
3. Established an observational facility in the rain shadow region of Western Ghats for clouds, precipitation for weather modification research. It was done with the following four facilities.
   1. Laboratory with c-band radar, microwave radiometer, wind profiler, ceilometer, and radiosonde for cloud studies, aerosol, cloud condensation nuclei (CCN), ice nucleating particle (INP) boundary layer flux measurements, etc.
   2. Rain gauge network of over 120 rain gauges in Solapur, Maharashtra for seeding evaluation and rainfall climatology over the region.
   3. Dynamical and thermodynamic measurements of boundary layer and cloud layer in colocation with c-band radar at Tuljapur, Maharashtra.
   4. Numerical modeling for cloud and precipitation assessment to assist decision making and evaluation for operational seeding.
4. Established a state-of-art lightning location network over India with about 75 sensors installed in several states. All sensors are integrated with the central processor at IITM. The network will be further expanded with a few more sensors to be added in the future.
5. Augmented measurements of aerosol chemistry, aerosol hygroscopicity, and volatile organic compounds (VOC) to improve the understanding of CCN activation process.
6. Conducted INP experiments to understand the variability at mixed-phase cloud conditions. Data sets are being used to develop ice nucleation parameterisation scheme and test.
7. Continued measurements of aerosol, cloud and precipitation to understand their interactions and orographic precipitation processes.
8. Established radar facilities in Mandhardev, Maharashtra (precipitation X-band since 2012 and cloud Ka-band since 2014] to understand the processes of orographic cloud and precipitation.
9. Used ice nucleation measurements to develop ice microphysics parameterisation and testing in the weather forecast model.
10. Established an Atmospheric Electricity Observatory (AEO) at Bharati, Indian station at Antarctica and SGU University Kolhapur, Maharashtra.
11. Measured aerosol, radiation, precipitation and cloud microphysics at HACPL, Mahabaleshwar, Maharashtra continuously since 2012.
12. Implemented measurements of VOC using a high-sensitivity proton transfer reaction mass spectrometer at HACPL to understand their role on secondary organic aerosols, and activation processes of CCN and ice nuclei.
13. Augmented hygroscopicity measurements for CCN closure studies and CCN parameterisation.
14. Provided technical guidance to the states on cloud seeding.
15. Established a Fluid Dynamics Laboratory with particle image velocimetry (PIV) systems and hot wire anemometry to study wall jets and shear flows.
16. Developed a mobile application named ‘Damini-Lightning Alert’.
17. Developed a mobile application-based meteorological dissemination system for Kumbh Mela.
18. Established a rain gauge network in Mumbai metropolitan region, and developed web-based data portal and mobile application to disseminate information for developing flood warning and forecast validation experiments to the public and MoES institutes.
19. Set up a MESO network of 40 ARGs in Mumbai metropolitan region.
20. Collated rainfall data from India Meteorological Department (IMD) and Municipal Corporation of Greater Mumbai (MCGM) into a web portal and mobile application.
21. Provided data of rainfall from ~100 stations to National Center for Coastal Research (NCCR), Chennai and National Center for Medium Range Weather Forecasting (NCMRWF), Noida for developing flood warning system and for forecast validation experiments.
22. Carried out winter fog field campaign at Indira Gandhi International (IGI) Airport, New Delhi in successive winter seasons. This was done to describe the environmental conditions in which fog develops, by measuring surface micrometeorology, radiation balance, turbulence, the thermodynamic structure of the surface layer, fog droplet, and aerosol microphysics fog water chemistry.
23. Established an observational facility for fog research at, IGI airport Delhi.
24. Developed an operational fog forecasting system at IGI airport, New Delhi.
25. Acquired 100 acres of land in Sehore from the government of Madhya Pradesh for establishment of Atmospheric Research Testbed in the core monsoon zone. Infrastructure development of ARThas commenced.

3. Atmospheric Research Testbeds (ART) for process studies and National Climate Reference Network (NCRN)

The ART program is a highly focused observational and analytical research effort that will use collocated observations from advanced measurement systems to understand various atmospheric processes, particularly cloud, land-atmosphere interactions and radiative processes, testing parameterisations of these processes for use in atmospheric models.

In the first phase, an ART would be set up in central India to study convection, land-atmosphere interactions and precipitation processes. It is expected to provide a sound basis for other research testbed programs in climatologically interesting and important areas. In the second phase, ART is to be set up in the northeast/eastern part of the country to study severe thunderstorm processes.

The NCRN will comprise of a network of ~25 climate reference stations commissioned in several parts of the country, equipped to provide long-term, accurate, and unbiased observations. Such observations are essential to define the state of the integrated Earth system, its history, and its future variability and change. Most historical in situ observations of surface climate variables have been undertaken for real-time applications such as weather forecasting, hydrology, and agro-meteorology. Changes during the time of observation, instrumentation, and operators also contribute to uncertainty. Historically, undocumented or inadequate metadata that describes changes in meteorological measurements has been all too common. The NCRN will make it possible to validate the climate projections from ESMs using accurate and reliable ground truth verifications.

Achievements of ART and NCRN

1. Acquired 100 acres of land in Silkheda, TahashilShyampur, Sehore, Madhya Pradesh (a core monsoon zone) to establish an ART. Planning and development of physical and equipment infrastructure are under progress.
2. Site selection for setting up climate reference stations is under progress.

4. Metro Air Quality and Weather Service (MAQWS)

MAQWS is a nearly warning system of air quality of Delhi. It was launched on 15th October 2018 and was developed in collaboration with the National Center for Atmospheric Research (NCAR), USA. The system assimilates data from ~36 monitoring stations which are run by the Central Pollution Control Board (CPCB), Delhi Pollution Control Committee (DPCC), and the System of Air Quality and Weather Forecasting and Research (SAFAR). The system provides location-specific information on air quality in near real-time and its forecast with a lead time of 1 to 3 days. Data from satellites on stubble burning in northwest India or dust storms along with the prevalent meteorological factors helps to improve the initial conditions of the dynamical chemistry transport model. This enables accurate prediction of air-quality, which aids in planning of mitigation strategies. The scientific outcome of MAQWS will allow the implementation of a graded response action plan well in advance. Such an approach could lead to more targeted and cost-effective action on weather information and clean air, focused on public health.

Achievements

1. Developed an advance version of Fine Dust Model with various dust schemes for large-scale dust storm and transport pathways to predict extreme events in northwest India including Delhi. Augmented it with SAFAR.
2. The first indigenous impact study of the impact of air pollution on health to understand exposure to particulate matter pollution and their effects on disease burden and life expectancy in each state of India.
3. Developed a first very high-resolution emission inventory of Delhi for 2018 and identified relative share of various sources.
4. Developed a dynamic gridded emission inventory of Kharif agriculture residue burning of north Indian states of Punjab and Haryana by synergising three satellite datasets (INSAT-3D, INSAT-3DR and MODIS data) with ground-real measurements. Quantified the contribution of stubble burning in Punjab and Haryana to the air quality of Delhi.
5. Developed a mechanism to understand prolonged air pollution emergency of Delhi in 2017 and its empirical relationship with fog.
6. Released advanced version of SAFAR-Air mobile application with multilingual and voice-enabled features for air quality forecast.

 

High Performance Computing System (HPCS)

MoES is mandated to provide the nation with the best possible services of forecasting the monsoons and other weather and climate parameters, ocean state, natural disasters such as earthquakes and tsunamis, and other phenomena related to earth systemnation. Improving these forecasts is a challenging task as it requires solving of complex mathematical equations at a very high spatial resolution across the entire globe. The models to solve these equations need high-performance computational resources, including modern supercomputers with vast parallel computing architecture with support from artificial intelligence and machine learning algorithms.

The existing HPCS resources of 6.8 petaflops (PF) commissioned in 2018 has resulted in improved short-medium scale forecasts with the usage of high-resolution models. For further enhancing weather and climate prediction, high-resolution dynamical models with increased complexity and advanced data assimilation techniques are required, which are highly computationally intensive. Rigorous developmental work has been undertaken at MoES institutes which include following important tasks.

MoES has taken up work to develop the following essential initiatives as part of the HPCS programme.

1. Enhance model resolution for weather and climate.
2. Improve forecasts in the short, medium and long-range scales for monsoon mission programs that involve sensitivity experiments for various physical processes.
3. Develop ensemble prediction models with more members.
4. Employ numerical techniques and atmosphere-ocean coupled model along with probabilistic forecasts with quantified uncertainty.

Adequate computational facilities are also required to enhance training capacity to cater to the enormous need for skilled manpower in the field of Earth System sciences. In addition, real-time weather and climate-related information and services are provided to the SAARC (South Asian Association for Regional Cooperation), IOR-ARC (Indian Ocean Rim Association for Regional Co-operation), RIMES (Regional Integrated Multi-hazard Early Warning System), ASEAN (Association of Southeast Asian Nations) countries for societal benefit.

MoES also hosts and established the Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) Centre for Weather and Climate, the Indo Africa Centre for Medium Range Weather Prediction in Mauritius, and the National Tsunami Early Warning Centre (which has been providing tsunami advisory services to India and IOR-ARC countries). High computational power is required to carry out all activities related to the flagship programs of MoES.

Objectives

1. Provide HPCS facility and extend support to neighbouring BIMSTEC countries, Indo-Africa Centre and other African countries.
2. Make computational resources available to academic and R&D community to improve forecast skill and work on the operational forecasting system.
3. Establish a comprehensive computational and visualization ecosystem to deliver state-of-the-art big-data analytics and artificial intelligence/machine learning framework.
4. Maintain HPCS facility by providing necessary infrastructures and support such as uninterruptible power source (UPS), cooling system, power and generator backup.

Implementing institutions

1. Indian Institute of Tropical Meteorology (IITM), Pune.
2. National Centre for Medium Range Weather Forecast (NCMRWF), Noida.

Achievements

1. The computational capacity of the HPCS was enhanced to ~1.3 PF in phase I (2013-14) by an additional 1.1 PF. The facility is established at IITM and NCMRWF with access to other units of MoES through dedicated NKN linkage.
2. HPC system of 4.0 PF was installed at IITM and of 2.8 PF was installed at NCMRWF in phase 2. The storage capacity of this system is 8 PB (petabyte) at IITM and 5.6 PB at NCMRWF.
3. The HPC system at NCMRWF is for operational and research activities of IMD, NCMRWF and Indian National Center for Ocean Information Services (INCOIS), Hyderabad.
4. The HPC system at IITM is mainly for research and operational runs of IMD for seasonal and extended range predictions and air quality early warning systems. It is also used to conduct Intergovernmental Panel on Climate Change (IPCC) climate projections and other historical and natural runs. A total of 16.7 PB (9.7 PB at IITM and 7.0 PB at NCMRWF) disk storage has been installed.
5. A total of 36 PB (27 PB at IITM and 19 PB at NCMRWF) tape library is available for storing historic data.
6. A 5 PB HPS Server has been deployed on pilot basis for long term data archival requirements at IITM and NCRMRWF.
7. HPC systems at NCMRWF and IITM were maintained at more than 99% uptime.
8. A mirror site was created at IITM for operational runs of INCOIS, IMD and NCMRWF as measures to mitigate un/planned shutdown.
9. Atmospheric Research Data Centre was maintained for various stakeholders, including scientists and citizens.

Plans

1. Augment HPC resources with at least 40 PF by 2022, 150 PF by 2025 and 500 PF by 2030.
2. Support research projects on parallelization and scalability of HPC resources and data centre design and practices.

 

MONSOON MISSION (MM-II)

Agricultural productivity and economy of our country largely depends on the performance of Indian monsoon rainfall. Therefore, prediction of total quantum of Indian summer monsoon rainfall (ISMR) during the months of June to September (also called the seasonal rainfall, which produces about 80% annual rainfall over the country), its intra-seasonal and inter-annual variability, and knowledge of extreme rainfall conditions are very useful for planning and managing agriculture, water resources and disaster management, leading to great benefit to the society and citizens of the country. The ISMR has a global teleconnection with El Nino, which relates to an anomalous warming of Sea Surface Temperature (SST) over East Pacific Ocean and its opposite phase La-Nina, relating to cooling of SST over the same region. This has a predictive value for seasonal prediction of ISMR, as the signal is obtained few months in advance.

In last few decades, many studies have been made on the El Nino and Southern Oscillation (ENSO) phenomenon which is a dominant mode of global inter-annual variability with vast influence on other regional climates. However, till a decade ago (up to 2010), no significant breakthrough had taken place in improving the prediction skill of the ISMR. Historically, statistical models had been used for operational long-range forecasts for the Indian summer monsoon rainfall over the years. But improvement in prediction skill was not appreciable in operational forecasts, in spite of better understanding of monsoon variability, its teleconnection mechanisms and the knowledge that it is a prominent heat source over Indian region that drives the major atmospheric circulations. Moreover, statistical models had constraints in predicting monsoon rainfall in higher spatial and temporal resolutions.

Recent improvements in dynamical numerical models with ocean-atmosphere coupling have shown good prediction skill of ENSO SST with six months lead time. The seasonal mean rainfall hind cast skill, at one season lead time, over the central Pacific is also very good. In recent times, with the dynamical models, several new approaches (high resolution, improved physical parameterization schemes, super parameterizations, data assimilation, etc.) have shown that the variability in tropics can be reasonably resolved, thereby creating optimism for improving the monsoon prediction. Although many centres in the world were using dynamical modelling frameworks to predict seasonal mean climate routinely, in India such a framework was not in place before 2012.

The Ministry of Earth Sciences (MoES), Government of India, launched the National Monsoon Mission (NMM) in 2012 (now referred as Monsoon Mission, MM), with a vision to develop a state-of-the-art dynamical prediction system for Indian monsoon rainfall on different time scales. MoES bestowed the responsibility of execution and coordination of this mission to the Indian Institute of Tropical Meteorology (IITM), Pune in collaboration with the National Centre for Environmental Prediction (NCEP), USA, other MoES organizations (NCMRWF, IMD & INCOIS) and various national and international academic institutions and organizations. Climate Forecast System (CFS) of NCEP was found to be one of the best among the currently available coupled climate models, and its second version (CFSv2) has been implemented at IITM Pune, as the basic modelling system for the above purpose. Scientists of IITM, along with collaborators, made necessary model development works on this base model for improving prediction skill of this model over Indian monsoon region, with decreased model bias. Unified Model (UM) of UK Meteorological Office was implemented at NCMRWF, Noida as the base model for short and medium range weather predictions. NCMRWF scientists and collaborators worked on this model. In addition, CFS & GFS based models were used for extended range prediction and high-resolution short-range prediction at IITM Pune, in collaboration with IMD and NCMRWF. Model data assimilation works were done at NCMRWF, INCOIS and IITM, in collaboration with the University of Maryland, USA. High Performance Super Computing Systems (HPCS), installed at IITM and NCMRWF, provided the modelling infrastructure. Several national and international projects were funded through MM and those were coordinated by the Monsoon Mission Directorate (MMD) at IITM, with guidance of important Committees formed by MoES. There had been many high-level training courses, manpower development works, deputation of young scientists abroad for working with international principal investigators, high-level meetings and events during the MM-I.

In 2017, the first phase of monsoon mission (referred to as MM-I) was completed successfully. The seasonal prediction system with improved hind cast skill (retrospective forecast of seasonal monsoon) was handed over to IMD for operational forecasting and this modified model is referred as Monsoon Mission CFS (MMCFS). The extended range prediction system was also handed over to IMD for operational forecasting of active/break spells of monsoon and other weather events, up to 4 weeks in advance. The success of MM-I led to its continuance, as the second phase.

The second phase of monsoon mission (MM-II), which began in September 2017, focuses on predicting weather/climate extremes and development of climatic applications based on monsoon forecasts, especially in the field of agriculture, hydrology and energy sector, while continuing model development activities. In MM-II, focus has been given to high-resolution short-range predictions, predicting extremes, and using forecasts to develop applications for agriculture, hydrology, disaster management, energy sector, etc. As a new initiative to predict extremes, dynamical prediction of thunderstorm and lightning has been initiated. Model development, through enhancement in resolution and improvement in physical processes in the model, is continuing for increasing prediction skill of Indian monsoon and minimizing model biases.

Objectives

1. To build a working partnership between the academic and R&D organizations, both national and international, to improve the operational monsoon forecast skill over the country.
2. To setup a state-of-the-art dynamical modelling frame work for improving prediction skill of ‘seasonal and extended range predictions’ and ‘short and medium range (up to two weeks) prediction’.

Implementing institutions

1. Indian Institute of Tropical Meteorology (IITM), Pune
2. National Centre for Medium Range Weather Forecast (NCMRWF), Noida
3. India Meteorological Department (IMD), New Delhi
4. Indian National Centre for Ocean Information Services (INCOIS), Hyderabad

Achievements

1. Short-range prediction: The world’s highest resolution Global Ensemble Forecast System (GEFS) for short range prediction at 12 km using 21 members of the model was developed by IITM and handed over to IMD for operationalization. The 12.5 km EPS based rainfall probability has enabled to initiate block level forecast of rainfall probability for IMD’s agromet application. The model has improved forecast of heavy rain and tropical cyclone track, intensity and landfall. The high-resolution forecast has also helped in forest fire outlook based on model prediction of soil moisture, rainfall and wind forecast.
2. Data Assimilation: Coupled ocean-atmospheric data assimilation system using Local Ensemble Transform Kalman Filter (LETKF) technique (weakly coupled) has been developed and implemented for CFSv2 at Aaditya HPC, IITM. Land data assimilation work has started at IITM recently.
3. Seamless Prediction system: Initiated a seamless prediction system version 0.0 by coupling MoM5 to the existing Monsoon Mission model.
4. Seasonal Prediction: During 2019-2020, the operational seasonal forecast of south west monsoon was prepared using Monsoon Mission Climate Forecast System (MMCFS), developed at IITM, Pune. Model development for climatic application in hydrology and agriculture by integrating river runoff in CFSv2 is in progress.
5. Extended Range Prediction: Developed strategy for the real-time extended range prediction of heat waves, a methodology to predict the Madden-Julian Oscillation in real-time on extended range, and improved genesis potential parameter to predict cyclogenesis in real-time.
6. Thunderstorm and lightning prediction system: New initiatives were taken up to develop a modelling framework for thunderstorm and lightning prediction using dynamical lightning parameterization (DLP) in WRF model.
7. Short-range high-resolution ensemble forecasting: Developed and operationalized percentile based (90th and 95th) extreme forecast of rainfall based on GFS (12 km) model, developed a probabilistic forecast for all the river basins of India based on GEFS ensemble forecast and operationalized by IMD’s flood monitoring offices (FMOs) for various river basins of India. GEFS based indices namely supercell composite parameter (SCP), wind gust index, and hail index have been developed for prediction of thunderstorm occurrence, wind gust, and hail.

Plans

1. Making very high-resolution forecasts by developing models to treat sub-grid scale phenomenon better than the present day models. (Approach should be observations → DNS → LES→GCM)
2. Developing tools (using artificial intelligence and machine learning algorithms) for generating 1-km weather forecasts and to enhance the skill of dynamical models at extended and seasonal time scales.
3. Incorporating new modules (wave model, ocean bio-geo chemistry, chemistry-aerosols etc.) in the climate models so that advisories of various products like potential fishing zones, ocean state forecasts, and other activities that can be initiated at seasonal and extended range time scales.

Atmospheric Observations Network

The scheme Atmospheric Observations Network of IMD is a continuing scheme primarily encompassing ongoing programs in an integrated manner aimed at sustenance of observational network. This scheme

is a part of the umbrella scheme Atmosphere & Climate Research-Modelling Observing Systems & Services (ACROSS) programme of MoES.

The meteorological services have significant societal impact. Public/private/government sectors demand for accurate prediction of weather and climate at various temporal and spatial scales is increasing due to possible impacts of global climate variability and change. Improved and reliable forecast of weather and climate requires high resolution dynamical models backed by comprehensive data assimilation systems. Thus, intensive monitoring of various weather systems through different platform based observing systems provide not only the necessary information about current weather systems, their effective assimilation in numerical models provide important guidance for skillful forecasts generation.

The current observations need to be sustained and continued as per WMO procedures and standards for all times to come. The measurement of various atmospheric parameters through surface, upper air, aircraft is a prime requirement for operating the meteorological services. Several major advanced technology based equipments have been installed over the years. The maintenance and augmentation of these equipments is essential so that the benefit of technology upgradation is available on continuous basis. IMD needs upgradation & sustenance of observational network in order to achieve accelerated progress for providing top quality meteorological services to the society. IMD has been operating and sustaining several types of observational networks all over the country for monitoring the meteorological conditions and providing the meteorological data to weather forecasting and other uses. Sustenance of integrated observation system will be the major strategy of the scheme.

Objectives

1. Sustenance and Augmentation of observational networks comprising of Doppler Weather Radars (DWRs), Automatic Rain Gauges (ARGs), Automatic Weather systems (AWSs), Upper Air, Surface and Environmental Observatories etc.
2. Sustenance & Establishment of Multi processing, computing and communication facilities for Satellite Meteorological Applications.

Weather & Climate Services

The scheme Weather & Climate Services of IMD is a continuing Scheme primarily encompassing ongoing programs in an integrated manner aimed at providing efficient weather and climate services across

IMD provides services to weather-sensitive sectors viz. agriculture, irrigation, shipping, aviation, offshore oil explorations, etc. Over the years, specialized services have also been built for state-of-the-art Monitoring, Detection and Early Warning of extreme weather phenomena including tropical cyclones, severe thunderstorms, dust storms, heavy rains and snowfall events, cold and heat waves, etc. The meteorological services have significant societal impact. Public/private/government sectors demand for accurate prediction of weather and climate at various temporal and spatial scales is increasing due to possible impacts of global climate variability and change. The weather services are dependent on the sustained investments in Research and Development (R&D) and capacity building so that advances in weather and climate sciences get inducted in to service through a focused performance evaluation in a semi-operational environment. Further improvement of current services requires effective conversion of R&D results into fully operational products, services and effective means to develop linkages with decision-makers and users. Especially, effective use of public weather services to communicate through tools, products and services that are useful for decision-making is the need of the hour.

Major components of the scheme “Weather and Climate Services” are:

1. Gramin Krishi Mausam Sewa (Agrometeorological Advisory Services)
2. Augmentation of Aviation Meteorological Services
3. Climate Services
4. Training in Operational Meteorology
5. Capacity Building

Objectives

1. Develop an Advanced Weather Prediction System for block level forecasts, skilful for next 3-5 days and develop advisories for sectors like Agriculture, Disaster Management, Water resources, Power, Tourism and Pilgrimage, Smart cities, Renewable Energy sector and Transport.
2. Setting up of District Agro-Met Units (DAMUs) in all the districts of the country for extension of Agromet Advisory Services (AAS).
3. To expand the outreach of weather based Agromet advisories to the 94 million farmers through multiple means of communication, collection of feedback and impact assessment of AAS.
4. Develop a state-of-the-Art support system for Aviation safety with the automated Aviation Weather Observing System and advanced forecasting tools for all the civil airports in the country.
5. Establishing new Aerodrome MET Offices at Greenfield Airports and setting up of automated Heliport Weather Observing & Transmitting System at Heliports, Landing ground and other strategic locations to support the Helicopter and low-level flight operation of Indian Air Force, Indian Army and also at important tourist and pilgrimage locations.
6. Establish a state-of-the-art Climate Data Centre with integrated advanced climate data services portal for rendering national and regional climate services. The climate data centre will provide a comprehensive set of improved and specialized climate services for the country through upgradation of the existing operational activities of climate monitoring, climate prediction, climate data management and climate application.
7. Provide appropriate climate services to South Asia as WMO recognized Regional Climate Centre (RCC) for the region.
8. To upgrade the training infrastructure and facilities to enhance the capacity of the training establishment to bear increased loads of long-term ab-initio training courses for new entrants, career progression courses and short term courses in specialized topics, training to the personnel from countries.
9. Contributions among WMO, Regional Integrated Multi-Hazard Early Warning System for Africa and Asia (RIMES)/ Economic and Social Commission for Asia and the Pacific (ESCAP)/ Global Framework for Climate
   Services (GFCS) in South Asia etc.
10. Conduct of workshops, Seminars, Trainings, Symposiums, Users meet, Advertising & Publicity, Outreach activities etc

Upgradation of Forecast System

The scheme Upgradation of Forecast System of IMD is a continuing Scheme primarily encompassing ongoing programs in an integrated manner aimed at providing efficient weather and climate services across the country in various sectors. This scheme is a part of the umbrella scheme “Atmosphere & Climate Research-Modelling Observing Systems & Services (ACROSS)” of MoES.

The proposed scheme Upgradation of Forecast System is aimed at improving the accuracy of weather forecasts to bring it at par with the international standards which will help many sectors like army operations, air operation, agriculture, tourism, mountaineering, aviation, roads and communications, power generation, water management, environmental studies, Sports & Adventure, Transport, Government Authorities, NGOs and Public in general.

Objectives

1. Upgradation and sustenance of Communication Systems for Data and Product transmission.
2. Development of an advanced Operational Forecast System, Delivery System for Forecast and other services.
3. Conduct of special campaign for improving Cyclone, Thunderstorm and Fog forecasting through Aircraft reconnaissance and provision of additional observations.
4. Integrated Himalayan Meteorological Programme for Western & Central Himalayas.
5. Capacity Building, Outreach, Planning and sustenance of specific process related observing systems over India.

Commissioning of Polarimetric Doppler Weather Radars (DWRs)

IMD presently operates a radar network most of which comprises of very old technology and are based on conventional analog systems, and therefore it is becoming obsolete with respect to the current and future generation DWRs. Moreover, the conventional radar products are incompatible with present day requirements of digital data on different parameters which can be directly used as inputs to weather prediction models.

Induction of an adequate number of DWRs in the network would facilitate plugging the existing gaps in the meteorological observational network of radars, desirable for effective and efficient analysis and consequent forecasting, in particular at the mesoscale. The availability of countrywide weather radar coverage and its integration, including overlapping regions of the proposed network would provide adequate warning in the event of approach of Cyclonic Storms, Monsoon Depressions, etc. It would also provide vital information for nowcasting purposes on mesoscale convective weather developments anywhere in the country. Radar observations would also stimulate research on the dynamics and microphysics of convective weather phenomena. The data from these DWRs would also help in understanding key as well as major differences between super cell storms and ordinary storms. Besides, it is desirable to have a dual polarimetric facility to obtain additional information on hydrometeors and their quantification in clouds, classification of precipitating clouds, etc.

Continuing the efforts of induction of the polarimetric DWRs with an aim of creating multiple overlapping configuration of the modern DWR network in the country, a total of another Eleven DWRs are being proposed by the IMD

Objectives

1. Improve upon the spatial and temporal density of Radar observational network, particularly over the regions with large data gaps in the country.
2. Better investigation, monitoring and tracking of Tropical Cyclones during pre-monsoon and post monsoon seasons in the subcontinent.
3. Better investigation, monitoring and tracking of Monsoon Depressions and Lows across the subcontinent.
4. Improvement in the current understanding of the physical processes associated with Tropical Cyclones and Monsoon Systems, including better understanding of convective activity/systems per-se.

These proposed set of new DWRs would be of immense use in better Nowcasting and mesoscale forecasting. Using both NWP and conventional approaches, the objectives of other ongoing programmes are also in sync with the current proposal’s objectives.