The growing data record from numerous geodetic observation techniques (e.g. satellite gravimetry, GNSS station observations, satellite radio occultation and reflectometry, satellite altimetry, etc.) allows a new quantitative view on various variables relevant for climate research such as terrestrial water storage, ice sheet and glacier mass, tropospheric water vapor, ocean currents or sea level. Geodetic methods provide unique information on the Earth' s surface geometry, its orientation and large scale mass transports caused by fluctuations in the Earth’s water cycle. Many of these observations can be related to Essential Climate Variables (ECV) according to the definition by the Global Climate Observing System (GCOS) and thus provide excellent tools to improve our understanding of climate related processes. Geodetic time series start to reveal a complex picture of natural climate variability, long term climate change, and anthropogenic modifications. As these data sets are independent of other data commonly used to drive and evaluate climate models, geodetic observations have strong potential for either being used as input for numerical models or for a posteriori model assessment. This presentation will give a general overview of the potential of geodetic observations for studying climate signals and will highlight some sepcific examples provided within the framework Inter-Comission Committee on "Geodesy for Climate Research" (ICCC) recently established by the International Association of Geodesy.

The Indian Summer Monsoon is the lifeline for India and surrounding countries. It effects every walk of life in this region from drinking water, agriculture, power generation and ultimately the well-being and GDP of the region. Studying and Predicting the Indian Summer Monsoon rainfall and its variability is a major challenge in weather and climate domain. AI/ML are useful tools for analysing and predicting major phenomena. In this talk we will discuss various techniques used for identifying patterns in the Indian Monsoon. We will also present various techniques developed for predicting the Monsoon on seasonal and intraseasonal scales using methods such as stacked auto-encoders etc. We will also discuss use of such techniques for predicting related phenomena such as the El-Nino etc. The results from these techniques would also be compared to other methods in use.  

The launch of the Sentinel-1 missions in 2014 boosted the availability of Synthetic Aperture Radar (SAR) images across the globe for earth observation. Besides, the radar signal's sensitivity to water vapor in the troposphere spawns an opportunity for high-resolution atmospheric monitoring at an unprecedented spatial scale. Interferometric analysis of the SAR images allows for a relatively precise determination of the vertically integrated water vapor between data acquisitions. Recent studies have put forward approaches to convert differential maps to total precipitable water vapor. Here, we apply a spatial wavelet signal analysis to isolate the differential water vapor signal at scales of tens of km from noise and systematic errors. We further investigate approaches suitable for separating the signal related to tropospheric water vapor from other signal components, such as changes in land cover and elevation. The validation tests will be performed using ERA-5 datasets and GNSS observations. Next, we study tropospheric water vapor states during the Monsoon season in Kerala, India. The region experienced a devastating flood event-related following the Monsoon rain in August 2018. We analyze the water vapor maps over Kerala to identify spatiotemporal patterns during the Monsoon season (namely April, May, June, and July months) in 2018 versus the years before and after, which did not lead to extreme flooding. The analysis improves our understanding of what kind of spatiotemporal atmospheric patterns increase risks of heavy rainfall and flooding during the monsoon season, and with that, it can benefit forecasting efforts. The results highlight the importance of radar-based water vapor maps and underscore the need to include SAR interferometric products into numerical meteorological models to enhance weather forecasts.

Water vapor is an important medium for moisture transmission which is crucial for the Earth’s water cycle. Atmospheric reanalyses are very promising data sources for the quantification of Integrated Water Vapor (IWV) with advantages of global spatial coverage, multi-decadal time scale, and unified assimilation strategy and models. Though various reanalysis products are developed, their performances on quantifying the IWV remain to be evaluated. Ground-based GPS (Global Positioning System) observations have been employed as an independent validation source for reanalyses, due to the fact that they are not included in the assimilation of reanalyses. China is one of the largest regions with typical monsoon climate. The climate of China is significantly influenced by the water vapor carried by East Asian Monsoon and Southwest Monsoon. The abnormality of the monsoons’ intensity, duration, and onset and/or retreat time can trigger severe floods or droughts. Therefore, it is important to characterize the spatiotemporal variations of the IWV over China accurately and reliably. In this work, we used IWV derived by more than 200 continuous GPS stations over China to assess various atmospheric reanalyses, such as the latest fifth-generation global reanalysis of European Centre for Medium-Range Weather Forecasts (ERA5), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), and the Japanese 55-year Reanalysis (JRA55). Then, we characterized the spatiotemporal variations of IWV over China by using GPS and reanalyses.

In the winter months, especially in December and January, fog disrupts life in North India, causing substantial economic and health loss. The suffering is enhanced due to a lack of timely and accurate predictions. Fog prediction is still challenging because of the limited understanding of the complex nature of fog. Fog is observed in two ways, either in-situ by observing visibility or remotely by satellite observations. Visibility and allied meteorological variables (e.g. temperature, relative humidity, wind speed) are used for in-situ identification of fog. In contrast, remote sensing of fog is based on the Brightness Temperature Difference concept. On one hand, in-situ observation is relatively more accurate but covers a limited spatial extent, while on the other hand, remote sensing of fog is less accurate but covers a larger area. Hence, these two methods are complementary to each other. The combined use of both methods can improve the understanding of fog. Since the underlying assumptions of the two methods are different, fog events identified by them may differ. The objective of the proposed study is to compare the fog events identified by the two methods. Fog events identified by in-situ visibility are compared with the events identified by Indian geostationary satellite INSAT-3D at a temporal resolution of 30 mins for 15 cities spread over North India during 2012-2020. The comparison of fog events identified by the two methods will help us understand the relative strengths and weaknesses of the two methods, leading to better fog identification.

The atmospheric lapse rate regulates the nature of heat flux exchanges within the atmospheric boundary layer thus affecting the weather and climatic conditions in a region and is an important input parameter in climate models. In recent years, the increase in greenhouse gas emission, has led to strong and uneven warming of the earth's atmosphere and the increase in aerosols emission have further affected it. Therefore, the objective of this study is to analyse the long-term trends (1979-2018) in the atmospheric lapse rate series at annual, seasonal, and monthly time scale across India using monthly mean temperature and geopotential data from ERA5 Reanalysis dataset. The Mann-Kendall trend test and the sequential Mann-Kendall test were used for estimation of trend as well as detection of sequential change in trend, respectively. The results show a decreasing trend in lapse rate values across India for most of the grid points. However, an increase in trend magnitude was observed in Eastern India during the monsoon months as compared to decrease in trend throughout the other seasons. The magnitude of trend varied between -15x 10^(-5) to 1x 10^(-5),  -8x 10^(-3) to 4x 10^(-3) and -1x 10^(-3) to 1x 10^(-3)  K per km per year for monthly, seasonal and annual time series, respectively. Further, at monthly time scale, significant change points were observed between 2010-15 for majority of India, while in southern part of India significant change points were observed between 1995-2000. Further, a correlation analysis was performed between atmospheric lapse rate, and greenhouse gas (CO₂) and aerosol concentration values at different grid points to identify the main factor responsible for the increase or decrease in lapse rate values during the study period.

Temporal variation in the gravity is being observed by GRACE/GRACE-FO mission since 2004, at different temporal scales, ranging from monthly to daily. The GRACE data at daily temporal resolution can be analyzed for its usability in various atmospheric and meteorological purposes. The minuscule variation in gravity is primarily due to the fluctuation of water content below, or on, or above the surface provided no major mass displacement event occurs. The water content responds to various weather effects like precipitation, seasons, storms, droughts, etc. The monsoon in India is a seasonal phenomenon that starts around the first week of June in the Southern part of the country and moves towards the northern parts. It takes one to two weeks for the same and in meantime, the movement is also responsible for the heavy precipitation. As the GRACE data is available at a daily scale hence it will be interesting to check whether the daily fluctuation in precipitation can be captured in it so that monsoon-related studies can be performed on GRACE data.