Comparison of ozonesonde measurements in the upper troposphere and lower Stratosphere in Northern India with reanalysis and chemistry-climate-model data

Abstract

The variability and trend of ozone (O3) in the Upper troposphere and Lower Stratosphere (UTLS) over the Asian region needs to be accurately quantified. Ozone in the UTLS radiatively heats this region and cools the upper parts of the stratosphere. This results in an impact on relative humidity, static stability in the UTLS region and tropical tropopause temperature. A major challenge for understanding ozone chemistry in the UTLS is sparse observations and thus the representation of precursor gases in model emission inventories. Here, we evaluate ozonesonde measurements during August 2016 at Nainital, in the Himalayas, against ozone from multiple reanalyses and the ECHAM6-HAMMOZ model. We find that compared to measurements both reanalyses and ECHAM6-HAMMOZ control simulation overestimate ozone mixing ratios in the troposphere (20 ppb) and in the UTLS (55 ppb). We performed sensitivity simulations using the ECHAM6-HAMMOZ model for a 50% reduction in the emission of (1) NOx and (2) VOCs. The model simulations with NOX reduction agree better with the ozonesonde observations in the lower troposphere and in the UTLS. Thus, neither reanalyses nor ECHAM6-HAMMOZ results can reproduce observed O3 over the South Asian region. For a better representation of O3 in the ECHAM6-HAMMOZ model, NOX emission should be reduced by 50% in the emission inventory. A larger number of observations of ozone and precursor gases over the South Asian region would improve the assessment of ozone chemistry in models.

Exploring an application-oriented land-based hyperspectral target detection framework based on 3D–2D CNN and transfer learning

“…Compared with traditional imaging methods, the advantage of hyperspectral images lies in their ability to accurately obtain diagnostic spectral features of the target, thereby excellently completing tasks such as pixel level classification [4][5][6], scene classification [7], and object detection [8,9]. Therefore, hyperspectral imaging technology is applied in both civilian and military fields, such as medicine [10,11], agriculture [12,13], environmental monitoring [14], and camouflage target detection [15]. Due to the limitations of imaging level, traditional hyperspectral target detection focuses more on quantitative analysis of spectral information [16].…”