ALBERT

Description: Climate change and air pollution have important societal consequences, especially in emerging economies, wherein transitions from polluting technologies to cleaner alternatives coincide with high population vulnerability to environmental threats. India is home to a fifth of the world’s population and a gamut of human activities, employing a far ranging spectrum of technologies and fuels, with consequent emissions. Atmospheric fine particles or aerosols in the region predominate in carbonaceous constituents and dust. Multi-institutional studies in the region have earlier focused on natural and anthropogenic climate forcing by aerosols and feedbacks on regional and global climate. Important gaps remain in understanding human activities influencing emissions, emission aerosol properties, and regional atmospheric processes, specifically those related to carbonaceous aerosol impacts on climate and air quality. With an aim to address these gaps, the COALESCE (Carbonaceous Aerosol Emissions, Source Apportionment and Climate Impacts) project was launched on 7 July 2017. The project adopts integration of scientific methods developed by both the climate and air quality research communities. New fundamental knowledge from the project and strong links to India’s policy framework would enable climate and clean-air action in the region. The article describes the scientific rationale, objectives, and planned activities under COALESCE to explore engagement with the international climate and air quality research communities and to enable eventual dissemination of research findings, knowledge products, and decision-support tools.

Description: We describe and show results from a series of field campaigns that used balloonborne instruments launched from India and Saudi Arabia during the summers 2014–17 to study the nature, formation, and impacts of the Asian Tropopause Aerosol Layer (ATAL). The campaign goals were to i) characterize the optical, physical, and chemical properties of the ATAL; ii) assess its impacts on water vapor and ozone; and iii) understand the role of convection in its formation. To address these objectives, we launched 68 balloons from four locations, one in Saudi Arabia and three in India, with payload weights ranging from 1.5 to 50 kg. We measured meteorological parameters; ozone; water vapor; and aerosol backscatter, concentration, volatility, and composition in the upper troposphere and lower stratosphere (UTLS) region. We found peaks in aerosol concentrations of up to 25 cm–3 for radii 〉 94 nm, associated with a scattering ratio at 940 nm of ∼1.9 near the cold-point tropopause. During medium-duration balloon flights near the tropopause, we collected aerosols and found, after offline ion chromatography analysis, the dominant presence of nitrate ions with a concentration of about 100 ng m–3. Deep convection was found to influence aerosol loadings 1 km above the cold-point tropopause. The Balloon Measurements of the Asian Tropopause Aerosol Layer (BATAL) project will continue for the next 3–4 years, and the results gathered will be used to formulate a future National Aeronautics and Space Administration–Indian Space Research Organisation (NASA–ISRO) airborne campaign with NASA high-altitude aircraft.

Description: The present study examines the role of tropical cyclones in the enhancement of tropospheric ozone. The most significant and new observation is the increase in the upper tropospheric (10–16 km) ozone by 20–50 ppbv, which has extended down to the middle (6–10 km) and lower troposphere (〈 6 km). The descending rate of enhanced ozone layer is found to be 0.87–1 km day−1. Numerical simulation of potential vorticity, vertical velocity and potential temperature indicate the intrusion of ozone from the upper troposphere to the surface. Space borne observations of relative humidity indicate the presence of sporadic dry air in the upper and middle troposphere over the cyclonic region. These observations constitute quantitatively an experimental evidence of enhanced tropospheric ozone during cyclonic storms.

Description: The retrieval of accurate profiles of temperature and water vapour is important for the study of atmospheric convection. Recent development in computational techniques motivated us to use adaptive techniques in the retrieval algorithms. In this work, we have used an adaptive neuro-fuzzy inference system (ANFIS) to retrieve profiles of temperature and humidity up to 10 km over the tropical station Gadanki (13.5° N, 79.2° E), India. ANFIS is trained by using observations of temperature and humidity measurements by co-located Meisei GPS radiosonde (henceforth referred to as radiosonde) and microwave brightness temperatures observed by radiometrics multichannel microwave radiometer MP3000 (MWR). ANFIS is trained by considering these observations during rainy and non-rainy days (ANFIS(RD + NRD)) and during non-rainy days only (ANFIS(NRD)). The comparison of ANFIS(RD + NRD) and ANFIS(NRD) profiles with independent radiosonde observations and profiles retrieved using multivariate linear regression (MVLR: RD + NRD and NRD) and artificial neural network (ANN) indicated that the errors in the ANFIS(RD + NRD) are less compared to other retrieval methods. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 92% for temperature profiles for all techniques and more than 99% for the ANFIS(RD + NRD) technique Therefore this new techniques is relatively better for the retrieval of temperature profiles. The comparison of bias, mean absolute error (MAE), RMSE and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and relative humidity (RH) profiles using ANN and ANFIS also indicated that profiles retrieved using ANFIS(RD + NRD) are significantly better compared to the ANN technique. The analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the temperature retrievals substantially; however, the retrieval of RH by all techniques considered in this paper (ANN, MVLR and ANFIS) has limited success.

Description: Study of Atmospheric Forcing and Responses (SAFAR) is a five year (2009–2014) research programme specifically to address the responses of the earth's atmosphere to both natural and anthropogenic forcings using a host of collocated instruments operational at the National Atmospheric Research Laboratory, Gadanki (13.5° N, 79.2° E), India from a unified viewpoint of studying the vertical coupling between the forcings and responses from surface layer to the ionosphere. As a prelude to the main program a pilot campaign was conducted at Gadanki during May–November 2008 using collocated observations from the MST radar, Rayleigh lidar, GPS balloonsonde, and instruments measuring aerosol, radiation and precipitation, and supporting satellite data. We show the importance of the large radiative heating caused by absorption of solar radiation by soot particles in the lower atmosphere, the observed high vertical winds in the convective updrafts extending up to tropopause, and the difficulty in simulating the same with existing models, the upward traveling waves in the middle atmosphere coupling the lower atmosphere with the upper atmosphere, their manifestation in the mesospheric temperature structure and inversion layers, the mesopause height extending up to 100 km, and the electro-dynamical coupling between mesosphere and the ionosphere which causes irregularities in the ionospheric F-region. The purpose of this communication is not only to share the knowledge that we gained from the SAFAR pilot campaign, but also to inform the international atmospheric science community about the SAFAR program as well as to extend our invitation to join in our journey.

Description: In the present study, we have used the Weather Research and Forecasting (WRF) model to simulate the features associated with a severe thunderstorm observed over Gadanki (13.5° N, 79.2° E), over southeast India, on 21 May 2008 and examined its sensitivity to four different microphysical (MP) schemes (Thompson, Lin, WSM6 and Morrison). We have used the WRF model with three nested domains with the innermost domain of 2 km grid spacing with explicit convection. The model was integrated for 36 h with the GFS initial conditions of 00:00 UTC, 21 May 2008. For validating simulated features of the thunderstorm, we have considered the vertical wind measurements made by the Indian MST radar installed at Gadanki, reflectivity profiles by the Doppler Weather Radar at Chennai, and automatic weather station data at Gadanki. There are major differences in the simulations of the thunderstorm among the MP schemes, in spite of using the same initial and boundary conditions and model configuration. First of all, all the four schemes simulated severe convection over Gadanki almost an hour before the observed storm. The DWR data suggested passage of two convective cores over Gadanki on 21 May, which was simulated by the model in all the four MP schemes. Comparatively, the Thompson scheme simulated the observed features of the updraft/downdraft cores reasonably well. However, all the four schemes underestimated strength and vertical extend of the updraft cores. The MP schemes also showed problems in simulating the downdrafts associated with the storm. While the Thompson scheme simulated surface rainfall distribution closer to observations, the other three schemes overestimated observed rainfall. However, all the four MP schemes simulated the surface wind variations associated with the thunderstorm reasonably well. The model simulated reflectivity profiles were consistent with the observed reflectivity profile, showing two convective cores. These features are consistent with the simulated condensate profiles, which peaked around 5–6 km. As the results are dependent on initial conditions, in simulations with different initial conditions, different schemes may become closer to observations. The present study suggests not only large sensitivity but also variability of the microphysical schemes in the simulations of the thunderstorm. The study also emphasizes the need for a comprehensive observational campaign using multi-observational platforms to improve the parameterization of the cloud microphysics and land surface processes over the Indian region.

Description: Retrieval of accurate profiles of temperature and water vapor is important for the study of atmospheric convection. However, it is challenging because of the uncertainties associated with direct measurement of atmospheric parameters during convection events using radiosonde and retrieval of remote-sensed observations from satellites. Recent developments in computational techniques motivated the use of adaptive techniques in the retrieval algorithms. In this work, we have used the Adaptive Neuro Fuzzy Inference System (ANFIS) to retrieve profiles of temperature and humidity over tropical station Gadanki (13.5° N, 79.2° E), India. The observations of brightness temperatures recorded by Radiometrics Multichannel Microwave Radiometer MP3000 for the period of June–September 2011 are used to model profiles of atmospheric parameters up to 10 km. The ultimate goal of this work is to use the ANFIS forecast model to retrieve atmospheric profiles accurately during the wet season of the Indian monsoon (JJAS) season and during heavy rainfall associated with tropical convections. The comparison analysis of the ANFIS model retrieval of temperature and relative humidity (RH) profiles with GPS-radiosonde observations and profiles retrieved using the Artificial Neural Network (ANN) algorithm indicates that errors in the ANFIS model are less even in the wet season, and retrievals using ANFIS are more reliable, making this technique the standard. The Pearson product movement correlation coefficient (r) between retrieved and observed profiles is more than 99% for temperature profiles for both techniques and therefore both techniques are successful in the retrieval of temperature profiles. However, in the case of RH the retrieval using ANFIS is found to be better. The comparison of mean absolute error (MAE), root mean square error (RMSE) and symmetric mean absolute percentage error (SMAPE) of retrieved temperature and RH profiles using ANN and ANFIS also indicates that profiles retrieved using ANFIS are significantly better compared to the ANN technique. The error analysis of profiles concludes that retrieved profiles using ANFIS techniques have improved the retrievals substantially; however, retrieval of RH by both techniques (ANN and ANFIS) has limited success.