This PDF file contains the front matter associated with SPIE Proceedings Volume 9882, including the Title Page, Copyright information, Table of Contents, and Conference Committee listing.

NASA has been providing global sea-surface temperatures (SST) from MODIS on daily to decadal periods, and these are extensively used for a wide range of atmospheric and oceanic studies. However, the retrieval quality and cloud detection are somewhat problematic. We will present a new physical deterministic algorithm based on truncated total least squares (TTLS) using multiple channels for SST retrieval from MODIS measurements in conjunction with a new cloud detection scheme using a radiative transfer model atop a functional spectral difference method. The TTLS method is a new addition to improve the information content of the retrieval compared to our earlier work using modified total least squares (MTLS). A systematic study is conducted to ascertain the optimal channel selection from the 16 channels in the thermal IR of MODIS. Our new algorithm can reduce average RMSE of ~50% while increasing the average data coverage by ~50% compared to the operationally available MODIS SST.

Ground and satellite based measurements show significant loading of atmospheric aerosols over the highly populated Indo-Gangetic Plains with implications to both air quality and regional climate. Recent studies have found varying trends in aerosol loading over this region during different seasons. However, most of these trends were associated or linked to changes in the strength of emission sources of both natural and anthropogenic origin. In this study, using data from multiple satellites (MODIS and MISR) and reanalysis (ECMWF, NCEP) products, we show that emission characteristics over the West or North-western part of India have significant impact on aerosol loading over the IGP irrespective of the seasons. Though it is known that variability in a combination of meteorological parameters impact aerosol loading conditions, we show that it is possible to explain them by using just the wind speed as a proxy. This shows that even slight changes to emission over Northwestern part of the Indian region may have significant impact on aerosol loading conditions over IGP with implications to air quality and regional climate.

Representation of rainfall distribution and monsoon circulation in the high resolution versions of NCMRWF Unified model (NCUM-REG) for the short-range forecasting of extreme rainfall event is vastly dependent on the key factors such as vertical cloud distribution, convection and convection/cloud relationship in the model. Hence it is highly relevant to evaluate the vertical structure of cloud and precipitation of the model over the monsoon environment. In this regard, we utilized the synergy of the capabilities of CloudSat data for long observational period, by conditioning it for the synoptic situation of the model simulation period. Simulations were run at 4-km grid length with the convective parameterization effectively switched off and on. Since the sample of CloudSat overpasses through the monsoon domain is small, the aforementioned methodology may qualitatively evaluate the vertical cloud structure for the model simulation period. It is envisaged that the present study will open up the possibility of further improvement in the high resolution version of NCUM in the tropics for the Indian summer monsoon associated rainfall events.

Elevated aerosols assume importance as the diabatic heating due to aerosol absorption is more intense at higher altitudes where the atmosphere becomes thinner. Indian region, especially its central and northern latitudes, experiences significant loading of elevated aerosols during pre-monsoon and summer months. Genesis of elevated aerosol loading over Indian region is investigated in the present study, using multi-year satellite observations from Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP) and Moderate Resolution Imaging Spectroradiometer (MODIS) along with reanalysis winds from MERRA. Central India is observed to have prominent aerosols loading at higher altitudes during pre-monsoon season, whereas it is during summer months over north-west India. Further analysis reveals that the elevated aerosols over Indian region in pre-monsoon and summer months are significantly contributed by transported mineral dust from the arid continental regions at west. In addition to the mineral dust advection, aerosols at higher altitudes over Indian region are enriched by strong convection and associated vertical transport of surface level aerosols. Vertical transport of aerosols observed over Indian region during pre-monsoon and summer months is aided by intense convergence at the surface level and divergence at the upper level. Moreover, aerosol source/sink strength estimated using aerosol flux continuity equation show significant aerosol production over central India during pre-monsoon. Strong vertical transport prevails during pre-monsoon uplifts the locally produced aerosols, with considerable anthropogenic fraction, to higher altitudes where their impacts would be more intense.

In this work, we have documented the characteristics of cloud hydrometeors over Indian region and surrounding oceans during pre-monsoon and monsoon seasons, using satellite data and cloud resolving model simulations. We have identified spatio-temporal variability of hydrometeors and explained them with the help of physical properties of the atmosphere. Further, we have performed cloud-resolving simulations of the WRF-Chem to understand the observed variability of cloud liquid water and rainfall. We have also found changes in cloud properties during high and low aerosol loading, leading to changes in the characteristics of intraseasonal oscillation of Indian summer monsoon.

Observing System Simulation Experiment (OSSE)s are a challenge to operational weather services, because many of the efforts offer long-term rather than short-term benefits. Effective interaction between Research and Operation (R2O and O2R) is required for successful OSSE.

First concept and procedures of OSSE are describer. Overview of OSSEs accomplished at NOAA/NCEP and JCSDA in recent years will be presented. Further proposed OSSEs are also presented.

Coupled Model Intercomparison Project phase 5 (CMIP5) coupled global climate model (CGCM) Representative Concentration Pathway (RCP) simulations project clear future temperature increase but diverse changes in Indian summer monsoon rainfall (ISMR) with substantial inter-model spread. Robust signals of projected changes are derived based on objective criteria and the physically consistent simulations with the highest reliability suggest future reduction in the frequency of light rainfall but increase in high to extreme rainfall. The role of equatorial Indian and Pacific Oceans on the projected changes in monsoon rainfall is investigated. The results of coupled model projections are also compared with the corresponding projections from high resolution AGCM time-slice, multi-physics and multi-forcing ensemble experiments.

Impact of SAPHIR radiance assimilation on the simulation of tropical cyclones over Indian region has been investigated using the Weather Research and Forecasting (WRF) model. Three cyclones that formed over Bay of Bengal have been considered in the present study. Assimilation methodology used here is the three dimensional variational (3DVar) scheme within the WRF model. With the initial and boundary conditions from Global Forecasting System (GFS) analyses from the National Centres for Environmental Prediction (NCEP), a control run (CTRL) without assimilation of any data and a 3DVar run with the assimilation of SAPHIR radiance have been performed. Both model simulations have been compared with the observations from India Meteorological Department (IMD), Tropical Rainfall Measurement Mission (TRMM), and analysis fields from GFS. Detailed analysis reveals that, the SAPHIR radiance assimilation has led to significant improvement in the simulation of all the three cyclones in terms of cyclone track, intensity, accumulated rainfall. The simulation of warm core structure and relative vorticity profile of each cyclone by 3DVar run are found to be more closer to GFS analyses, when compared with the CTRL run.

This study demonstrates the added benefits of assimilating the Advanced Technology Microwave Sounder (ATMS) radiances from the Suomi-NPP satellite in the NCMRWF Unified Model (NCUM). ATMS is a cross-track scanning microwave radiometer inherited the legacy of two very successful instrument namely, Advanced Microwave Sounding Unit-A (AMSU-A) and Microwave Humidity Sounder (MHS). ATMS has 22 channels: 11 temperature sounding channels around 50-60 GHz oxygen band and 6 moisture sounding channels around the 183GHz water vapour band in addition to 5 channels sensitive to the surface in clear conditions, or to water vapour, rain, and cloud when conditions are not clear (at 23, 31, 50, 51 and 89 GHz). Before operational assimilation of any new observation by NWP centres it is standard practice to assess data quality with respect to NWP model background (short-forecast) fields. Quality of all channels is estimated against the model background and the biases are computed and compared against that from the similar observations. The impact of the ATMS data on global analyses and forecasts is tested by adding the ATMS data in the NCUM Observation Processing system (OPS) and 4D-Var variational assimilation (VAR) system. This paper also discusses the pre-operational numerical experiments conducted to assess the impact of ATMS radiances in the NCUM assimilation system. It is noted that the performance of ATMS is stable and it contributes to the performance of the model, complimenting observations from other instruments.

Performance of an EnVar hybrid data assimilation system based on 3D Var NGFS (NCMRWF Global Forecast System) of T574 configuration and Ensemble Kalman Filter is investigated. The experiment is conducted during the Indian monsoon season (June-September) 2015 and compared against operational GSI 3D Var system. Two way coupled dual resolution hybrid system with 80 member ensemble of T254L64 configuration are used and forecasts are done for 10days. In hybrid experiment 75% weight is given to ensemble covariance and 25% for static covariance. The forecast skill of experiments over different spatial domains is compared against observations and respective analysis. The hybrid experiment produced significant improvement in forecasts compared to 3D Var in all fields except lower level temperature over tropical regions. Improvement is also seen in the prediction of extreme rainfall events. The prediction of monsoon onset and track of cyclone Ashobaa with hybrid and 3D var system is discussed.

During Indian summer monsoon (ISM) season, nearly about half of the monsoonal rainfall is brought inland by the low pressure systems called as Monsoon Depressions (MDs). These systems bear large amount of rainfall and frequently give copious amount of rainfall over land regions, therefore accurate forecast of these synoptic scale systems at short time scale can help in disaster management, flood relief, food safety. The goal of this study is to investigate, whether an accurate moisture–rainfall feedback from land surface can improve the prediction of inland moving MDs. High Resolution Land Data Assimilation System (HRLDAS) is used to generate improved land state .i.e. soil moisture and soil temperature profiles by means of NOAH-MP land-surface model. Validation of the model simulated basic atmospheric parameters at surface layer and troposphere reveals that the incursion of high resolution land state yields least Root Mean Squared Error (RMSE) with a higher correlation coefficient and facilitates accurate depiction of MDs. Rainfall verification shows that HRLDAS simulations are spatially and quantitatively in more agreement with the observations and the improved surface characteristics could result in the realistic reproduction of the storm spatial structure, movement as well as intensity. These results signify the necessity of investigating more into the land surface-rainfall feedbacks through modifications in moisture flux convergence within the storm.

Few systematic attempts to interpret the measurements of mm-wave radiometers over clouds and precipitation have been made to date because the scattering signatures of hydrometeors at these frequencies are very difficult to model. The few algorithms that have been developed try to retrieve surface precipitation, to which the observations are partially correlated but not directly sensitive. In fact, over deep clouds, mm-wave radiometers are most sensitive to the scattering from solid hydrometeors within the upper levels of the cloud. In addition, mm-wave radiometers have a definite advantage over the lower-frequency window-channel radiometers in that they have finer resolution and can therefore explicitly resolve deep convection. Preliminary analyses (in particular of NOAA's MHS brightness temperatures, as well as Megha-Tropiques's SAPHIR observations) indicate that the measurements are indeed very sensitive to the depth and intensity of convection. The challenge is to derive a robust approach to make quantitative estimates of the convection, for example the height and depth of the condensed water, directly from the mm-wave observations, as a function of horizontal location. To avoid having to rely on a specific set of microphysical assumptions, this analysis exploits the substantial amount of nearly- simultaneous coincident observations by mm-wave radiometers and orbiting atmospheric profiling radars in order to enforce unbiased consistency between the calculated brightness temperatures and the radar and radiometer observations.

Current study investigates the impact of changes in land use/land cover (Lu/Lc) on thunderstorm, the short lived convective event occurred in New Delhi. We are trying to understand the impact of urban Lu/Lc changes on the structure and evolution of severe thunderstorm activities over a short time period. Lu/Lc data from IGBP are available for the period 1992-1993 and recent period 2012-2013 Lu/Lc data are from the ISRO AWiFS satellite sensor. We have used a cloud resolving model at 1.5 km resolution embedded within a coarser resolution global model at 17 km resolution. These configurations of models are based on UK Met Office Unified Model. Recent period Lu/Lc shows an increase in urban build up and increase in bare soil fraction over Delhi region. Our result shows that the Lu/LC change can impact the low level wind and thermodynamic structure of the storm.

This study investigates the aerosols-rainfall interaction during Indian summer monsoon and characterizes their difference in drought and excess summer monsoon years, based on MODIS (MODerate Resolution Imaging Spectro-radiometer) derived Aerosol Optical Depth (AOD) at 550 nm. AOD has been estimated using Level-2 MODIS Terra Data Version 6. AOD in drought years is found to be higher over India compared to excess monsoon years. The total effect of aerosols causes reduction of summer rainfall but with distinct differences in their impact during strong and weak summer monsoon years, due to the changes in clouds, radiation, large-scale circulation, and convection. Aerosol and cloud characteristics exhibit strong association to rainfall variability in interannual time scales. Variability in cloud effective radius and cloud optical thickness is found to be consistent with aerosol effect.

In this study we investigated the impact of land surface surface process & land-atmospheric interaction on weather and surface hydrology. The ultimate goal is to integrate remote sense data into numerical mesoscale weather prediction and regional climate model in order to improve prediction of the impacts of land-atmosphere interactions and land-surface processes on regional weather, and hydrology. We have used climatology based green vegetation fraction and 8-day Moderate Resolution Imaging Spectroradiometer (MODIS) based green vegetation fraction and integrated in the Land Information System to conduct uncoupled simulation to understand the impact on surface and hydrological parameters in the summer season. The vegetation response is also realized through coupled regional climate simulation in which climatological based greenness and 8-days varying vegetation is investigated and quantify the impact of vegetation on summertime precipitation process. This study has bought following findings (a) Satellite based vegetation indices captures vegetation temporal patterns more realistic than climatological vegetation data and detects early/late spring signature through vegetation indices, (b) Integrated satellite vegetation greenness input data in regional weather model resolved much better soil moisture and soil temperature including the diurnal cycle of surface heat fluxes and surface temperature in the simulation. Secondly, integration of the TRMM based satellite rainfall product into coupled hydrological and Atmospheric model and results shows better resolved soil moisture patterns in the remote regions of the Asia Mountain regions.

Inspite of significant improvement in numerical model physics, resolution and numerics, the general circulation models (GCMs) find it difficult to simulate realistic seasonal and intraseasonal variabilities over global tropics and particularly over Indian summer monsoon (ISM) region. The bias is mainly attributed to the improper representation of physical processes. Among all the processes, the cloud and convective processes appear to play a major role in modulating model bias. In recent times, NCEP CFSv2 model is being adopted under Monsoon Mission for dynamical monsoon forecast over Indian region. The analyses of climate free run of CFSv2 in two resolutions namely at T126 and T382, show largely similar bias in simulating seasonal rainfall, in capturing the intraseasonal variability at different scales over the global tropics and also in capturing tropical waves. Thus, the biases of CFSv2 indicate a deficiency in model’s parameterization of cloud and convective processes. Keeping this in background and also for the need to improve the model fidelity, two approaches have been adopted. Firstly, in the superparameterization, 32 cloud resolving models each with a horizontal resolution of 4 km are embedded in each GCM (CFSv2) grid and the conventional sub-grid scale convective parameterization is deactivated. This is done to demonstrate the role of resolving cloud processes which otherwise remain unresolved. The superparameterized CFSv2 (SP-CFS) is developed on a coarser version T62. The model is integrated for six and half years in climate free run mode being initialised from 16 May 2008. The analyses reveal that SP-CFS simulates a significantly improved mean state as compared to default CFS. The systematic bias of lesser rainfall over Indian land mass, colder troposphere has substantially been improved. Most importantly the convectively coupled equatorial waves and the eastward propagating MJO has been found to be simulated with more fidelity in SP-CFS. The reason of such betterment in model mean state has been found to be due to the systematic improvement in moisture field, temperature profile and moist instability. The model also has better simulated the cloud and rainfall relation. This initiative demonstrates the role of cloud processes on the mean state of coupled GCM. As the superparameterization approach is computationally expensive, so in another approach, the conventional Simplified Arakawa Schubert (SAS) scheme is replaced by a revised SAS scheme (RSAS) and also the old and simplified cloud scheme of Zhao-Karr (1997) has been replaced by WSM6 in CFSV2 (hereafter CFS-CR). The primary objective of such modifications is to improve the distribution of convective rain in the model by using RSAS and the grid-scale or the large scale nonconvective rain by WSM6. The WSM6 computes the tendency of six class (water vapour, cloud water, ice, snow, graupel, rain water) hydrometeors at each of the model grid and contributes in the low, middle and high cloud fraction. By incorporating WSM6, for the first time in a global climate model, we are able to show a reasonable simulation of cloud ice and cloud liquid water distribution vertically and spatially as compared to Cloudsat observations. The CFS-CR has also showed improvement in simulating annual rainfall cycle and intraseasonal variability over the ISM region. These improvements in CFS-CR are likely to be associated with improvement of the convective and stratiform rainfall distribution in the model.

These initiatives clearly address a long standing issue of resolving the cloud processes in climate model and demonstrate that the improved cloud and convective process paramterizations can eventually reduce the systematic bias and improve the model fidelity.

Tropical cyclones (TCs) have strong impact on socio-economic conditions of the countries like India, Bangladesh and Myanmar owing to its awful devastating power. This brings in the need of precise forecasting system to predict the tracks and intensities of TCs accurately well in advance. However, it has been a great challenge for major operational meteorological centers over the years. Genesis of TCs over data sparse warm Tropical Ocean adds more difficulty to this. Weak and misplaced vortices at initial time are one of the prime sources of track and intensity errors in the Numerical Weather Prediction (NWP) models. Many previous studies have reported the forecast skill of track and intensity of TC improved due to the assimilation of satellite data along with vortex initialization (VI). Keeping this in mind, an attempt has been made to investigate the impact of vortex initialization for simulation of TC using UK-Met office global model, operational at NCMRWF (NCUM). This assessment is carried out by taking the case of a extremely severe cyclonic storm "Chapala" that occurred over Arabian Sea (AS) from 28th October to 3rd November 2015. Two numerical experiments viz. Vort-GTS (Assimilation of GTS observations with VI) and Vort-RAD (Same as Vort-GTS with assimilation of satellite data) are carried out. This vortex initialization study in NCUM model is first of its type over North Indian Ocean (NIO). The model simulation of TC is carried out with five different initial conditions through 24 hour cycles for both the experiments. The results indicate that the vortex initialization with assimilation of satellite data has a positive impact on the track and intensity forecast, landfall time and position error of the TCs.

Tropical cyclone prediction, in terms of intensification and movement, is important for disaster management and mitigation. Hitherto, research studies were focused on this issue that lead to improvement in numerical models, initial data with data assimilation, physical parameterizations and application of ensemble prediction. Weather Research and Forecasting (WRF) model is the state-of-art model for cyclone prediction. In the present study, prediction of tropical cyclone (Phailin, 2013) that formed in the North Indian Ocean (NIO) with and without data assimilation using WRF model has been made to assess impacts of data assimilation. WRF model was designed to have nested two domains of 15 and 5 km resolutions. In the present study, numerical experiments are made without and with the assimilation of scatterometer winds, and radiances from ATOVS and ATMS. The model performance was assessed in respect to the movement and intensification of cyclone. ATOVS data assimilation experiment had produced the best prediction with least errors less than 100 km up to 60 hours and producing pre-deepening and deepening periods accurately. The Control and SCAT wind assimilation experiments have shown good track but the errors were 150-200 km and gradual deepening from the beginning itself instead of sudden deepening.

Operational short range prediction of Meso-scale thunderstorms for Sriharikota(13.7°N ,80.18°E) has been performed using two nested domains 27 & 9Km configuration of Weather Research & Forecasting-Advanced Research Weather Model (WRF- ARW V3.4).Thunderstorm is a Mesoscale system with spatial scale of few kilometers to a couple of 100 kilometers and time scale of less than an one hour to several hours, which produces heavy rain, lightning, thunder, surface wind squalls and down-bursts.

Numerical study of Thunderstorms at Sriharikota and its neighborhood have been discussed with its antecedent thermodynamic stability indices and Parameters that are usually favorable for the development of convective instability based on WRF ARW model predictions. Instability is a prerequisite for the occurrence of severe weather, the greater the instability, the greater will be the potential of thunderstorm. In the present study, K Index, Total totals Index (TTI), Convective Available Potential Energy (CAPE), Convective Inhibition Energy (CINE), Lifted Index (LI), Precipitable Water (PW), etc. are the instability indices used for the short range prediction of thunderstorms. In this study we have made an attempt to estimate the skill of WRF ARW predictability and diagnosed three thunderstorms that occurred during the late evening to late night of 31st July, 20th September and 2nd October of 2015 over Sriharikota Island which are validated with Local Electric Field Mill (EFM), rainfall observations and Chennai Doppler Weather Radar products. The model predicted thermodynamic indices (CAPE, CINE, K Index, LI, TTI and PW) over Sriharikota which act as good indicators for severe thunderstorm activity.

The accurate estimation of water vapour with high spatial and temporal resolution is needed for operational weather forecasts and weather and climate research. Moisture representation in numerical weather prediction models is inadequate for forecasting meso-scale precipitation events and accurate information of middle and upper tropospheric moisture determines strength, effectiveness and longevity deep convective processes. Global Navigation Satellite System (GNSS) provides a way for measuring atmospheric humidity continuously at a low operational cost by co-locating GPS receiver with meteorological sensor. Impact of assimilation of GPS-IPW observations from NOAA-NWS and EUMETNET network on NCMRWF GFS forecast is investigated during May-June period in 2014. Impact of assimilation of GPS-IPW observations are not only confined to the regions of dense GPS-IPW network, but can be seen in other regions also. Ingestion of IPW observations impacted prediction of rainfall over the Indian monsoon region even though very few IPW stations located in the region. Impact of assimilation is not uniform on temperature, wind and humidity and different over different region. GPS-IPW observations can impact forecast of individual rainfall events at large and major impact on rainfall forecast is seen in the regions of large integrated precipitable water in the model. In India MoES has already setup many GPS-IPW stations and also some more are in the pipeline . The quality of these present observations from MoES and plans for the future GPS-IPW stations are discussed.

Aerosols are tiny suspended particle in the atmosphere with high variability in time and space, play a major role in modulating the cloud properties and thereby precipitation. To understand the aerosol induced Invigoration effect predictors like aerosol optical depth, cloud optical depth, cloud top temperature, cloud effective radii, ice water path, retrieved from the Moderate resolution Imaging Spectroradiometer (MODIS) level-3 aqua satellite data were analysed for pre monsoon April-May and post monsoon October-November months over the Indian subcontinent 8 ° N to 33° N, 65 °E to 100 °E during the period 2003–2013. Apart from the above data, mesoscale dynamical parameters such as vertical wind shear of horizontal wind, relative humidity, were also considered to understand their role in invigoration.

Case studies have been carried out for the regions having heavy rainfall events & minimal rainfall events during high Aerosol optical depths occasions respectively. Analysis revealed that the heavy rainfall which occurred in this region with higher optical depths might be due to invigoration effect of aerosols wherein the dynamical as well as thermodynamical parameters were also found favourable. Minimal rainfall events were also observed most probably due to the suppression of rain formation/delay in precipitation due to high amount of aerosol concentration in these regions. Prominent 36 such cases were studied all over India during Pre & Post monsoon months.

The socioeconomic aspects of life in coastal regions of India are significantly affected by tropical cyclones (TCs) over North Indian Ocean (NIO). It is well known that the lack of conventional observation over the ocean is a critical factor limiting the accuracy of the TC forecast. The goal of this study is to assess the impact of hyperspectral sounder measurements from Atmospheric Infrared Sounder (AIRS) and Infrared Atmospheric Sounding Interferometer (IASI) in the MetOp satellite on TC simulation using NCMRWF Unified Model (NCUM) with 17 km horizontal resolution. The results of the study indicate that the assimilation of hyperspectral radiance data has a positive impact on the prediction of track and intensity of TC.

Sea surface wind vectors from Advanced Scatterometer (ASCAT) onboard MetOP satellites are pivotal inputs to NWP models, especially during cyclone period. NCMRWF regularly receives these winds from NOAA through ftp and routinely assimilates in the operational global models. The impact of ASCAT winds in the NCMRWF Unified Model (NCUM) assimilation and forecast system during the cyclone Chapala period from 28 October 2015 to 4 November 2015 is studied. Before assimilating, these winds are validated against in-situ observations from buoy platforms over the North Indian Ocean (NIO). It is found that the errors in the ASCAT winds are well within the limit of mission goal (<2m/s). After the successful validation, numerical experiments are designed in such a way that 10-day forecasts are generated from two different initial conditions. In the control run (CTL), ASCAT winds are removed from the Observation Processing System (OPS) and Variational Assimilation (VAR) systems of NCUM, while in the experiment run (AST) ASCAT winds are included in both OPS and VAR. Forecasts from both the runs are analysed to see the movement and intensification of the cyclonic system in due course. The results show that the experiments with ASCAT winds improved the track and intensity of the NIO cyclonic system.

Megha-Tropiques (MT) is an Indo-French Joint Satellite Mission, launched on 12 October 2011. MT-SAPHIR is a sounding instrument with 6 channels near the absorption band of water vapor at 183 GHz, for studying the water cycle and energy exchanges in the tropics. The main objective of this mission is to understand the life cycle of convective systems that influence the tropical weather and climate and their role in associated energy and moisture budget of the atmosphere in tropical regions. India also has a prestigious space programme and has launched the INSAT-3D satellite on 26 July 2013 which has an atmospheric sounder for the first time along with improved VHRR imager. NCMRWF (National Centre for Medium Range Weather Forecasting) is regularly receiving these new datasets and also making changes to its Global Data Assimilation Forecasting (GDAF) system from time-to-time to assimilate these new datasets. A well planned strategy involving various steps such as monitoring of data quality, development of observation operator and quality control procedures, and finally then studying its impact on forecasts is developed to include new observations in global data analysis system. By employing this strategy observations having positive impact on forecast quality such as MT-SAPHIR, and INSAT-3D Clear Sky Radiance (CSR) products are identified and being assimilated in the Global Data Assimilation and Forecasting (GDAF) system.

Scattering phase function plays a crucial role in studies and calculations based on radiative transfer theory in water as well as atmosphere. A model based on Mie theory is developed for estimating the particulates-in-water scattering phase function for forward angles (0.1° - 90°). Particle size distribution (PSD) slope (ξ) and bulk refractive index (n) are chosen as key inputs for this proposed model. The PSD slope can be estimated from the attenuation spectrum measured directly in-situ and the bulk refractive index can be calculated by an inversion model using measured backscattering ratio (B_P) and PSD slope. The attenuation spectrum and backscattering ratio can be easily measured in-situ using commercially available instruments in real time. The entire range of forward angles is divided into two ranges and phase function is modeled separately in the ranges 0.1° - 5° and 5° - 90°, from numerically calculated Volume Scattering Function (VSF) using Mie theory. The division boundary is decided owing to the fact that the scattering phase functions, for different oceanic conditions, exhibit a change in slope at approximately 5°. Performance of the present model is evaluated by comparing with existing empirical and analytical models as well as measured phase functions. The proposed phase function model shows a considerable improvement upon existing models, and will have important applications in remote sensing applications and underwater studies.

Aquarius is mission that aims to measure Sea surface salinity (SSS) from the space in order to provide the global salinity for climate studies. Accurate estimation of SSS is useful for the hydrological cycle, oceanographic processes, and climate. Recently the new version (V4) of Aquarius data releases with the improving the quality of the data and achieving the mission accuracy requirement globally on monthly scale. This paper highlight the results of recently release Aquarius V4, and version 3 (V3) data with Argo observations on monthly time scale from 2012-2014 periods. The spatial distribution of mean SSS shows that both products capture the SSS variation very well. The Aquarius V4 SSS showed minor improvement over the Aquarius V3 SSS with less root mean square error over the central & eastern equatorial Indian Ocean (EEIO) & part of Bay of Bengal (BOB). The frequency distribution is also improved in Aquarius V4 compare to Aquarius V3 average over the different regions. However, both the versions overestimates/underestimates the frequency of low/high salinity values.

Warm core is the characteristic that distinguishes tropical cyclones from its extra tropical counter parts, where the center of the cyclone is warmer than its environment. Two of the most common variables used to characterize the warm core are its strength and height. The strength is given by the magnitude of maximum perturbation temperature and the height is the level where the maximum perturbation temperature occurs. INSAT-3D, India's advanced weather satellite, is the first geostationary sounder over India and the surrounding Oceanic regions. INSAT-3D has 18 channel sounder with a resolution of 10 km to profile the atmospheric temperature and humidity. Brightness Temperatures (Tbs) from INSAT-3D sounder channels are used to analyze the warm core structure of Tropical cyclone Phailin (8–14 October 2013) over the North Indian Ocean. Only when the system becomes very severe cyclonic system, when the eye of the cyclone is clearer (fully cloud free), the sounder channel Tbs showed multiple maxima, with strong primary maximum in the middle level (600–500 mb) and the secondary maximum in the upper level (300–250 mb), unlike the conventional belief suggested warm core existence at 250 mb. Due to the high resolution of (10 km) INSAT-3D sounder channels, compared to the Micro wave channels (AMSU-A of 50 km resolution), the warm core structure below 10 km of the atmosphere is well resolved.

Land use/land cover (LU/LC) changes are considered to be one of the most important factors affecting regional climate and are thus an area of public concern. The land surface plays a crucial role in boundary layer evolution and precipitation patterns thereby establishing the need for LU/LC inputs as a critical part of modeling systems. Inaccurate LU/LC information often leads to very large errors in surface energy fluxes thus leading to errors in boundary layer state. We have investigated an incident of heavy rainfall during August 2015 over West Bengal, India using Weather Research and Forecast (WRF) model by incorporating different LU/LC datasets, IRS P6 Advanced Wide Field Sensor (AWiFS) LU/LC data for 2012-13 and the default Moderate Resolution Imaging Spectro-radiometer (MODIS) derived USGS LU/LC data for 2001. In the present study, we have made a comparative assessment between AWiFS derived LU/LC and USGS LU/LC by incorporating these datasets as one of the lower boundary conditions over Indian region in WRF model version 3.5.1 to simulate, at 10km resolution, a heavy rainfall event associated with landfall of a cyclonic system over West Bengal. The results of the study suggested influence of LU/LC in occurrence of heavy rainfall with WRF model using AWiFS LU/LC showing more realistic simulation as AWiFS LU/LC is more up-to-date and features recent changes in LU/LC over India.

Last decade has seen a tremendous improvement in the forecasting skill of numerical weather prediction (NWP) models. This is attributed to increased sophistication in NWP models, which resolve complex physical processes, advanced data assimilation, increased grid resolution and satellite observations. However, prediction of heavy rains is still a challenge since the models exhibit large error in amounts as well as spatial and temporal distribution. Two state-of-art NWP models have been investigated over the Indian monsoon region to assess their ability in predicting the heavy rainfall events. The unified model operational at National Center for Medium Range Weather Forecasting (NCUM) and the unified model operational at the Australian Bureau of Meteorology (Australian Community Climate and Earth-System Simulator — Global (ACCESS-G)) are used in this study. The recent (JJAS 2015) Indian monsoon season witnessed 6 depressions and 2 cyclonic storms which resulted in heavy rains and flooding. The CRA method of verification allows the decomposition of forecast errors in terms of error in the rainfall volume, pattern and location. The case by case study using CRA technique shows that contribution to the rainfall errors come from pattern and displacement is large while contribution due to error in predicted rainfall volume is least.

An attempt is made here to evaluate the skill of forecast during boreal summer monsoon regime over the Indian region using the Observation Simulation System Experiment (OSSE) with Doppler Wind LIDAR (DWL) onboard International Space Station (ISS), assimilated in the initial condition. Through various techniques such as pattern correlation, root mean square error etc, we found that there is some positive impact of assimilating the DWL data on the forecast particularly at the lower tropospheric level. Impact on lowering the RMSE is seen for wind fields in the 850 and 500 hPa over Indian domain but not much impact is seen over larger domain. The moisture field and cloud also show marginal impact due to assimilation of DWL. This indicates that possibly due to lower spatial resolution of DWL data and more data gap over Indian and surrounding oceanic region, the impact on forecast is less. However, it shows the promise that monsoon being a convectively coupled system; increase in spatial data by DWL may better resolve the low level wind and subsequently the low level shear which is important for convection trigger in boundary layer.

An Atmospheric/Tropospheric River (AR/TR) is a relatively narrow corridor of concentrated moisture where horizontal transport occurs in the lower atmosphere. They transport moisture from tropical regions towards the poles across the mid latitudes. Research of Atmospheric River over the Indian Monsoon region is not reported in literature. In this paper an attempt is made to examine the existence of AR in Indian Ocean and surrounding region. Meteorological parameters such as precipitable water, rainfall, air temperature and wind have been analyzed for the same. Analysis shows a clear evidence of the presence of Atmospheric River during the pre-monsoon and monsoon period. It is seen that there are variations in the origin, orientation, duration and also the formation of the river according to the vapor content in the Indian Ocean. During Elnino phase there is a pronounced transport of moisture through an Atmospheric River and also a high intensity transport occurs during monsoon period (JJA), even if moisture prevails over Indian monsoon region during other seasons also. Detailed results and extension to model forecasts will be presented in the paper.

Thunderstorms are mesoscale convective systems of towering cumulonimbus clouds of high vertical and horizontal extent lasting from a few minutes to several hours. Pre-monsoon thundershowers over the past 10 years have been analyzed to understand the organization, horizontal and vertical development and dissipation of such severe events. Kalbaisakhi’s/ Norwester’s over north east and East India is given preference in this study, while some of the other extreme events are also analyzed due to their severity. The meteorological parameters like horizontal and vertical wind, precipitable water etc., and derived variables such as Severe Weather Threat (SWEAT) Index, Convective Available Potential Energy (CAPE), and Convective Inhibition Energy (CINE) of the identified cases are analyzed using observations from NCEP and IMD. Satellite observations from IMD and TRMM are also used to analyze the development and moisture flow of such systems. The analysis shows that some of the parameters display a clear signature of developing thunderstorms. It is also seen that cloud parameters such as convective precipitation rate and convective cloud cover from NCEP FNL didn’t show much variation during the development of storms, which may be attributed to the limitation of spatial and temporal resolution. The parameters which showed indications of a developing thunderstorm were studied in detail in order to understand the possible mechanisms behind the development and organization of thunderstorm cells.

Indian Summer Monsoon Rainfall (ISMR) of 2015 showed a deficit of 14% in the seasonal rainfall. Many researchers connected this deficit to the El-Nino which developed in late May. In this study an analysis of major ENSO events and its influence on ISMR during the period 1975 till present have been carried out. The behavior of ISMR during the previous El-Nino/La-Nina years has been compared with that of 2015. Preliminary analysis shows the effects of El-Nino on ISMR of 2015 started mainly from July. This is attributed to Madden Julian Oscillation (MJO) by many scientists. Analysis of spatial and temporal correlations of SST of various Nino regions with the ISMR and of MJO will also be presented in detail.

Hooghly is one of the major estuaries in Ganges, the largest and longest river in the Indian subcontinent. The Hooghly estuary is a coastal plain estuary lying approximately between 21°–23° N and 87°–89° E. We used a terrain following ocean model to study tide driven residual circulations, seasonal mean flow patterns and its energetics in the Hooghly estuary and adjacent coastal oceans on the north eastern continental shelf of India. The model is driven by tidal levels at open ocean end and winds at the air-sea interface. The sources of forcing fields for tides were from FES2012, winds from ECMWF. Harmonic analysis is carried out to compute the tidal and non-tidal components of currents and sea level from the model solutions. The de-tidal components were averaged for the entire period of simulation to describe residual and mean-seasonal circulations in the regions. We used tide-gauge, SARAL-ALTIKA along track sea level measurements to evaluate model solutions. Satellite measure Chla were used along with simulated currents to describe important features of the circulations in the region.

The monsoon onset over Kerala is considered as the beginning of rain fall over India and it is the end of hot summer. Different criteria have been used to define the monsoon onset over Kerala, with the one given by India Meteorological Department taken as the standard criteria. The analysis of the past 20 years of observations shows that the variables during the monsoon onset do not behave in the same way always. The purpose of this paper is to analyses the monsoon onset phases and to find possible reasons behind the variable nature of the monsoon onset. Different meteorological parameters like precipitation, outgoing long wave radiation (OLR), winds, air temperature, and specific humidity at different levels are analyzed for the same. Research has been done on various distinct features of monsoon such as Low Level Jet (LLJ), Tropical Easterly Jet (TEJ), monsoon trough, and depressions etc., during the onset phase. The analysis showed that in some years the strength of LLJ is lesser compared to the normal years. It is also seen that in some years the wind flow pattern is different from that observed during a standard onset year. The results of these analyses will be presented in detail in the paper.

Fog is a meteorological phenomenon that causes reduction in regional visibility and affects air quality, thus leading to various societal and economic implications, especially disrupting air and rail transportation. The persistent and widespread winter fog impacts the entire the Indo-Gangetic Plains (IGP), as frequently observed in satellite imagery. The IGP is a densely populated region in south Asia, inhabiting about 1/6th of the world’s population, with a strong upward pollution trend. In this study, we have used multi-spectral radiances and aerosol/cloud retrievals from Terra/Aqua MODIS data for developing an automated web-based fog monitoring system over the IGP. Using our previous and existing methodologies, and ongoing algorithm development for the detection of fog and retrieval of associated microphysical properties (e.g. fog droplet effective radius), we characterize the widespread fog detection during both daytime and nighttime. Specifically, for the night time fog detection, the algorithm employs a satellite-based bi-spectral brightness temperature difference technique between two spectral channels: MODIS band-22 (3.9μm) and band-31 (10.75μm). Further, we are extending our algorithm development to geostationary satellites, for providing continuous monitoring of the spatial-temporal variation of fog. We anticipate that the ongoing and future development of a fog monitoring system would be of assistance to air, rail and vehicular transportation management, as well as for dissemination of fog information to government agencies and general public. The outputs of fog detection algorithm and related aerosol/cloud parameters are operationally disseminated via http://fogsouthasia.com/.

This study uses precipitation estimates from the Tropical Rainfall Measuring Mission to estimate the intensity and examine the spatiotemporal patterns in the modes found in intraseasonal timescale over the Indian monsoon region during boreal summer. Here, using multichannel singular spectrum analysis, two dominant modes of oscillations are found in the intraseasonal timescale with periodicity of 10–20-days and 20–60-days, respectively. 20–60-days mode shows northward propagation from the equatorial Indian Ocean linked with the eastward propagating modes of convective systems over the tropics. 10–20-days mode shows very complex structure with a northwestward propagating anomaly pattern emanating from the Indonesian coast moving towards central India. This pattern is found to have a possible interaction with a structure emerging from higher latitudes propagating southeastwards. The two intraseasonal modes contribute comparable amount to the total rainfall variability. The intensity of the 20–60-days (10–20-days) mode show significantly strong inverse (direct) relationship with all- India June–September rainfall and both the modes exhibit profound variability in their intensity in interannual scale. This study also establishes that the probability of getting good amount of rainfall (no rainfall) over central India increases significantly if the two intraseasonal modes exhibit positive (negative) anomalies over the region. Relation between the ISO intensities and sea surface temperature is also discussed. This study points towards the fact that the knowledge of ISO phases can increase the skill in the probabilistic forecasting of rainfall over India.

Ocean Heat Content (OHC) plays a significant role in modulating the intensity of Tropical Cyclones (TC) in terms of the oceanic energy available to TCs. TC Heat Potential (TCHP), an estimate of OHC, is thus known to be a useful indicator of TC genesis and intensification. In the present study, we analyze the role of TCHP in intensification of TCs in the North Indian Ocean (NIO) through statistical comparisons between TCHP and Cyclone Intensities (CI). A total of 27 TCs (20 in the Bay of Bengal, and 7 in the Arabian Sea) during the period 2005–2012 have been analyzed using TCHP data from Global Ocean Data Assimilation System (GODAS) model of Indian National Center for Ocean Information Services and cyclone best track data from India Meteorological Department. Out of the 27 cyclones analyzed, 58% (86%) in the Bay (Arabian Sea) have negative correlation and 42% (14%) cyclones have positive correlation between CI and TCHP. On the whole, more than 60% cyclones in the NIO show negative correlations between CI and TCHP. The negative percentage further increases for TCHP leading CI by 24 and 48 hours. Similar trend is also seen with satellite derived TCHP data obtained from National Remote Sensing Center and TC best track data from Joint Typhoon Warming Centre. Hence, it is postulated that TCHP alone need not be the only significant oceanographic parameter, apart from sea surface temperature, responsible for intensification and propagation of TCs in the NIO.