National Atmospheric Research Laboratory

Abstract. Satellite observations of column-averaged carbon dioxide (XCO2) and methane (XCH4) mixing ratios provide essential data for monitoring greenhouse gas (GHG) emissions. However, the accuracy of emission estimates depends on the precision and bias of satellite retrievals, which require validation against ground-based reference measurements. This study presents a systematic validation of XCO2 and XCH4 data from GOSAT (Greenhouse gases Observing SATellite) and OCO-2 (Oribiting Carbon Observatory-2) satellites over South India using ground-based Fourier transform spectrometer (FTS) observations at Gadanki (13.5° N, 79.2° E) collected from October 2015 to July 2016. Satellite products from National Institute for Environmental Studies, Japan (NIES), NASA's Atmospheric CO2 Observations from Space (ACOS) project, USA (ACOS), and the University of Leicester, UK (UoL) were evaluated using a three-step spatial-temporal pairing method. Results show that the UoL's proxy XCH4 product meets the European Space Agency's Climate Change Initiative (ESA CCI) bias requirement (<10 ppb) across all spatial windows, while the NIES XCH4 product meets the requirement only for intermediate spatial scales. For XCO2, NASA ACOS and OCO-2 products meet the CCI bias requirement (<0.5 ppm), while NIES XCO2 exceeds this threshold. All products satisfy the precision requirement (<8 ppm for XCO2 and <34 ppb for XCH4) with substantial margins. In addition, FLEXPART model simulations using regional emission inventories revealed that agricultural activities dominate seasonal methane enhancements, contributing approximately 55 %, followed by waste and wetland emissions. The model captured seasonal trends but underestimated the amplitude of observed variations, highlighting the influence of changing background methane levels. These findings demonstrate the suitability of recent satellite products for regional GHG monitoring and emphasise the need for expanding ground-based FTS networks across South Asia to support improved emission assessments.

Abstract A dye-sensitized solar cell (DSSC) consists of four crucial components: a photoanode, electrolyte, sensitizer, and photocathode. The stability, efficiency, and sustainability of a DSSC rely on these components. In this study, a biopolymer (chitosan) with graphite filler has been utilized as the electrolyte system to address sealing and leakage issues associated with liquid electrolytes. Chitosan, with its β(1–4) linked 2-amino-deoxy-D-glucopyranose units, exhibits a polycationic character that enhances anionic interactions, forming a polyelectrolyte complex. Furthermore, chitosan is biodegradable, eco-friendly, biocompatible, and non-toxic, making it a sustainable choice. The TiO₂ working electrode has been improved with CuO nanopowder to minimize the inherent energy barrier. A cocktail dye, prepared from beetroot and spinach dyes in a 1:1 ratio, is used as the sensitizer, replacing synthetic dyes and enhancing the eco-friendliness of the fabricated DSSC. The reported solar conversion efficiency is approximately 2.3%, with a fill factor of 54%, under an irradiation of 100 mW/cm².

Helium (He) owing to its low mass and large scale-height, exhibits a unique behaviour, including the formation of He bulges, their latitudinal and seasonal variability [1,3], and their response to Global Dust Storms (GDS) [2]. In particular, the latitudinal shift of northern winter bulge observed during Martian year (MY) 34 GDS [2] highlights its sensitivity to the extreme dust conditions in the lower atmosphere and also demonstrates coupling between the lower and upper atmosphere of Mars. Recently, [2] compared He density observations from the Neutral Gas and Ion Mass Spectrometer (NGIMS) onboard NASA's Mars Atmosphere and Volatile Evolution (MAVEN) mission with the He density simulations from Mars Global Ionosphere-Thermosphere Model (M-GITM). The result of the study revealed a strong agreement between the dayside observations of He density and a Gravity Waves (GWs) parameterized version of M-GITM, pointing towards the crucial role of GWs in He bulge formation. However, on the nightside the data-model comparison shows significant difference between the observations and simulations. These differences are associated with a static GWs parameterization scheme used in M-GITM. To our best knowledge, no other Global Climate Models (GCMs) simulations have been compared with NGIMS He observations, for the period of the MY34 GDS. Consequently, He bulge response to a GDS using a GW-parameterized GCMs remains understudied.This study uses a multi-model approach to examine the impact of MY34 GDS and GW induced momentum and energy deposition on He bulges for the pre (Ls ~ 184°), peak (Ls ~ 207°) and decay (Ls ~ 240°) phases of the GDS. We simulate He densities under MY34 dust conditions using M-GITM and Mars Planetary Climate Model (Mars-PCM). We compare these simulations with the He bulges simulated using “Climatology” dust scenario. In M-GITM, MY33 dust maps are used as proxy for the climatology dust scenario. Whereas for Mars-PCM, “climatology” dust maps are derived by averaging dust opacities observed during MY 24 to 35, excluding MY 25, 28, and 34 [4]. The M-GITM simulations are analysed at an altitude of 200 km and Mars-PCM simulations are 1.37x10-8 pressure level pseudo altitude (~200 km).The result of this study shows a strong agreement between M-GITM and Mars-PCM simulations. The northern poleward shift of He bulge during MY34 GDS is a GW influenced seasonal pattern and not a direct effect of the GDS, as suggested in previous studies. In addition, simulations indicate a distorted dayside-retained bulge during the peak-phase of the storm. These distortions likely result from enhanced damping of meridional circulation at polar latitudes, driven by GWs, which also suppress He bulges during high-dust seasons. Thus, the results of this study further our understanding of Impact of MY34 GDS on He bulges, highlighting the significance of GW induced changes particularly at the high latitude regions in the upper atmosphere of Mars.Acknowledgments: Funding for this project was provided by the UAE University under the Grant G00003407. The Mars PCM used in this work can be downloaded from the SVN repository at https://svn.lmd.jussieu.fr/Planeto/trunk/LMDZ.MARS/. The model installation process is explained in https://svn.lmd.jussieu.fr/Planeto/LMDZ.MARS/user_manual.pdf. The M-GITM simulations used in this study are archived on the university of Michigan Deep Blue Data repository (e.g. K. Roeten & Bougher, 2022).

<strong class="journal-contentHeaderColor">Abstract.</strong> Cloud fraction (CF) is an integral aspect of weather and radiation forecasting, but real time monitoring of CF is still inaccurate, expensive and exclusive to commercial sky imagers. Traditional cloud segmentation methods, which often rely on empirically determined threshold values, struggle under complex atmospheric and cloud conditions. This study investigates the use of a Random Forest (RF) classifier for pixel-wise cloud segmentation using a dataset of semantically annotated images from five geographically diverse locations. The RF model was trained on diverse sky conditions and atmospheric loads, ensuring robust performance across varied environments. The accuracy score was always above 85 % for all the locations along with similarly high F1 score and Receiver Operating Characteristic &ndash; Area Under the Curve (ROC-AUC) score establishing the efficacy of the model. Validation experiments conducted at three Atmospheric Radiation Measurement (ARM) sites and two Indian locations, including Gadanki and Merak, demonstrated that the RF classifier outperformed conventional Total Sky Imager (TSI) methods, particularly in high-pollution areas. The model effectively captured long-term weather and cloud patterns, exhibiting strong location-agnostic performance. However, challenges in distinguishing sun glares and cirrus clouds due to annotation limitations were noted. Despite these minor issues, the RF classifier shows significant promise for accurate and adaptable cloud cover estimation, making it a valuable tool in climate studies.

Abstract. We investigate the formation mechanism of a tropopause cirrus cloud layer observed during the Balloon measurement campaigns of the Asian Tropopause Aerosol Layer (BATAL) over Hyderabad (17.47° N, 78.58° E), India, on 23 August 2017. Simultaneous measurements from a backscatter sonde and an optical particle counter on board a balloon flight revealed the presence of a subvisible cirrus cloud layer (optical thickness ∼ 0.025) at the cold-point tropopause (temperature ∼ −86.4 °C, altitude ∼ 17.9 km). Ice crystals in this layer are smaller than 50 µm with a layer mean ice crystal number concentration of about 46.79 L−1. Simultaneous backscatter and extinction coefficient measurements allowed us to estimate the range-resolved extinction to backscatter coefficient ratio (lidar ratio) inside this layer with a layer mean value of about 32.18 ± 6.73 sr, which is in good agreement with earlier reported values at similar cirrus cloud temperatures. The formation mechanism responsible for this tropopause cirrus is investigated using a combination of three-dimensional back trajectories, satellite observations, and ERA5 reanalysis data. Satellite observations revealed that the overshooting convection associated with a category 3 typhoon, Hato, which hit Macau and Hong Kong on 23 August 2017, injected ice into the lower stratosphere. This caused a hydration patch that followed the Asian summer monsoon anticyclone to subsequently move towards Hyderabad. The presence of tropopause cirrus cloud layers in the cold temperature anomalies and updrafts along the back trajectories suggested the role of typhoon-induced waves in their formation. This case study highlights the role of typhoons in influencing the formation of tropopause cirrus clouds through stratospheric hydration and waves.