RESUMEN

The tropospheric delay caused by the temporal and spatial variation of meteorological parameters is the main error source in interferometric synthetic aperture radar (InSAR) applications for geodesy. To minimize the impact of tropospheric delay errors, it is necessary to select the appropriate tropospheric delay correction method for different regions. In this study, the interferogram results of the InSAR, corrected for tropospheric delay using the Linear, Generic Atmospheric Correction Online Service for InSAR (GACOS) and ERA-5 atmospheric reanalysis dataset (ERA5) methods, are presented for the study area of the junction of the Hengduan Mountains and the Yunnan-Kweichow Plateau, which is significantly influenced by the plateau monsoon climate. Four representative regions, Eryuan, Binchuan, Dali, and Yangbi, are selected for the study and analysis. The phase standard deviation (STD), phase-height correlation, and global navigation satellite system (GNSS) data were used to evaluate the effect of tropospheric delay correction by integrating topographic, seasonal, and meteorological factors. The results show that all three methods can attenuate the tropospheric delay, but the correction effect varies with spatial and temporal characteristics.

RESUMEN

Historical biomass burning in summer season (April‒June, during 2015‒2021) was assessed by studying active fire spot data recorded by the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard NASA/NOAA Suomi NPP satellite and mapping the same over Indian landmass. The fire spots often formed regional clusters and most profusely covered the states of Odisha, Chhattisgarh, Andhra Pradesh, Telengana, Madhya Pradesh, Maharashtra, Jharkhand, Karnataka, Punjab, Uttar Pradesh, Haryana, Gujarat, Tamil Nadu, Manipur, Nagaland, and Mizoram during April but their number decreased conspicuously in May and further in June. Forward movements of air masses potentially carrying fire-induced air pollutants from five principal fire cluster regions (northern, south eastern, western, north-eastern, and central) of India during April and May in 2021 were traced by 6-day forward airtrajectory modelling. It was observed that many parts of India were the recipients of air coming from the above principal fire clusters. In each year, the surface mass concentration of black carbon (BC), one of the most prominent markers of biomass burning, was higher in April over May and June in the affected regions, commensurate with the most active period of fire. The BC surface mass concentrations progressively declined thereafter in May and June with decreasing number of active fire spots along with declining average monthly height of the planetary boundary layer (PBL), indicating integral connection of surface BC levels with biomass burning. The study suggests that in spite of more favourable meteorological conditions in summer, extensive biomass burning may have had a crucial role to play in perturbing local and regional air quality over India by generating BC and other air pollutants.

RESUMEN

Vegetation phenology is a sensitive indicator of climate change and vegetation growth. In the present study, two phenological phases with respect to vegetation growth at the initial and mature stages, namely, the start of the season (SOS) and the peak of the season (POS), were estimated from a satellite-derived normalized difference vegetation index (NDVI) dataset over a long-term period of 32 years (1983 to 2014) and used to explore their responses to atmospheric variables, including air temperature, precipitation, solar radiation, wind speed and soil moisture. First, the forward feature selection method was used to determine whether each independent variable was linear or nonlinear to the SOS and POS. In addition, a generalized additive model (GAM) was used to analyze the correlation between the phenological phases and each independent variable at different temporal scales. The results show that soil moisture and precipitation are linearly correlated with the SOS, whereas the other variables are nonlinearly correlated. Meanwhile, soil moisture, wind speed and solar radiation are found to be nonlinearly correlated with the POS. However, air temperature and precipitation reveal a significant negative correlation with the POS. Furthermore, it was concluded that the aforementioned independent variables from the previous year could contribute to approximately 63%-85% of the SOS variations in the present year, whereas the atmospheric variables from April to June could contribute to approximately 70%-85% of the POS variations in the same year. Finally, the SOS and POS predicted by the GAM exhibit significant agreement with those derived from the satellite NDVI dataset, with the root mean square error of approximately 3 to 5 days.

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RESUMEN

The ocean interacts with the atmosphere via interfacial exchanges of momentum, heat (via radiation and convection), and fresh water (via evaporation and precipitation). These fluxes, or exchanges, constitute the ocean-surface energy and water budgets and define the ocean's role in Earth's climate and its variability on both short and long timescales. However, direct flux measurements are available only at limited locations. Air-sea fluxes are commonly estimated from bulk flux parameterization using flux-related near-surface meteorological variables (winds, sea and air temperatures, and humidity) that are available from buoys, ships, satellite remote sensing, numerical weather prediction models, and/or a combination of any of these sources. Uncertainties in parameterization-based flux estimates are large, and when they are integrated over the ocean basins, they cause a large imbalance in the global-ocean budgets. Despite the significant progress that has been made in quantifying surface fluxes in the past 30 years, achieving a global closure of ocean-surface energy and water budgets remains a challenge for flux products constructed from all data sources. This review provides a personal perspective on three questions: First, to what extent can time-series measurements from air-sea buoys be used as benchmarks for accuracy and reliability in the context of the budget closures? Second, what is the dominant source of uncertainties for surface flux products, the flux-related variables or the bulk flux algorithms? And third, given the coupling between the energy and water cycles, precipitation and surface radiation can act as twin budget constraints-are the community-standard precipitation and surface radiation products pairwise compatible?

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