Dynamics of Amphan Cyclone and Associated Changes in Ocean, Land Meteorological and Atmospheric Parameters

The low-pressure system developed in the Bay of Bengal and the Andaman Sea during March- October, often forms tropical cyclones, depending upon the intensity widespread destruction occurs in the areas where landfall takes place along the Indian coastal region. On 20 May, 2020, tropical cyclone Amphan hit the Indian coast at Bakkhali, West Bengal, in the afternoon (1330 IST). On 19 May, 2020, the intensity strengthened into a super cyclonic storm, with a strong wind speed up to 220 km/h. This cyclone affected a large population of India and Bangladesh. More than twenty-two thousand houses were damaged and millions of people were shifted to a safe place and due to the spread of COVID-19, the rescue missions were quite challenging. The cyclone affected most of the eastern states of India, heavy rainfall occurred causing floods along the track of cyclones. Using multi-satellite, ground and Argo floats data, we have analyzed meteorological and atmospheric parameters during May 2020. Our detailed analysis shows pronounced changes in atmospheric (CO mole fraction, total ozone column) and ocean parameters (chlorophyll concentration, dissolved oxygen, salinity, sea surface and sub-surface temperature) before and after the cyclone. Changes in ocean parameters such as caused by the cyclone Amphan along its track and the atmospheric and meteorological parameters change as the cyclone moves over the land

Changes in Atmospheric, Meteorological, and Ocean Parameters Associated with the 12 January 2020 Taal Volcanic Eruption

The Taal volcano erupted on 12 January 2020, the first time since 1977. About 35 mild earthquakes (magnitude greater than 4.0) were observed on 12 January 2020 induced from the eruption. In the present paper, we analyzed optical properties of volcanic aerosols, volcanic gas emission, ocean parameters using multi-satellite sensors, namely, MODIS (Moderate Resolution Imaging Spectroradiometer), AIRS (Atmospheric Infrared Sounder), OMI (Ozone Monitoring Instrument), TROPOMI (TROPOspheric Monitoring Instrument) and ground observations, namely, Argo, and AERONET (AErosol RObotic NETwork) data. Our detailed analysis shows pronounced changes in all the parameters, which mainly occurred in the western and south-western regions because the airmass of the Taal volcano spreads westward according to the analysis of airmass trajectories and wind directions. The presence of finer particles has been observed by analyzing aerosol properties that can be attributed to the volcanic plume after the eruption. We have also observed an enhancement in SO2, CO, and water vapor, and a decrease in Ozone after a few days of the eruption. The unusual variations in salinity, sea temperature, and surface latent heat flux have been observed as a result of the ash from the Taal volcano in the south-west and south-east over the ocean. Our results demonstrate that the observations combining satellite with ground data could provide important information about the changes in the atmosphere, meteorology, and ocean parameters associated with the Taal volcanic eruption

A new high-resolution sea surface temperature blended analysis

The National Oceanic and Atmospheric Administration’s (NOAA) office of National Environmental Satellite, Data, and Information Service (NESDIS) now generates a daily 0.05° (∼5 km) global high-resolution satellite-based sea surface temperature (SST) analyses on an operational basis. The new analysis combines SST data from U.S., Japanese, and European geostationary infrared imagers, and low-Earth-orbiting infrared (United States and Europe) SST data, into a single high-resolution 5-km product. An earlier version produced a 0.1° (∼11 km) resolution, a resolution chosen to approximate the Nyquist sampling criterion for the midlatitude Rossby radius (∼20 km), in order to preserve mesoscale oceanographic features such as eddies and frontal meanders. Comparison between the two analyses illustrates that the higher-resolution grid spacing has more success in this regard. The analysis employs a rigorous multiscale optimum interpolation (OI) methodology that approximates the Kalman filter, together with a data-adaptive correlation length scale, to ensure a good balance between detail preservation and noise reduction. The product accuracy verified against globally distributed buoys is ∼0.02 K, with a robust standard deviation of ∼0.25 K. The new analysis has proven a significant success even when compared to other products that purport to have a similar resolution. This analysis forms the basis for other operational environmental products such as coral reef bleaching risk and ocean heat content for tropical cyclone prediction. Forthcoming enhancements include the incorporation of microwave SST products from low-Earth-orbiting platforms [e.g., Global Change Observation Mission for Water-1 (GCOM-W1)] in order to improve the resolution of SST features in areas of persistent cloud and correct for diurnal effects via a turbulence model of upper-ocean heating

Towards Dependence of Tropical Cyclone Intensity on Sea Surface Temperature and Its Response in a Warming World

Tropical Cyclone (TC) systems affect global ocean heat transport due to mixing of the upper ocean and impact climate dynamics. A higher Sea Surface Temperature (SST), other influencing factors remaining supportive, fuels TC genesis and intensification. The atmospheric thermodynamic profile, especially the sea-air temperature contrast (SAT), also contributes due to heat transfer and affects TC’s maximum surface wind speed (Vmax) explained by enthalpy exchange processes. Studies have shown that SST can approximately be used as a proxy for SAT. As a part of an ongoing effort in this work, we simplistically explored the connection between SST and Vmax from a climatological perspective. Subsequently, estimated Vmax is applied to compute Power Dissipation Index (an upper limit on TC’s destructive potential). The model is developed using long-term observational SST reconstructions employed on three independent SST datasets and validated against an established model. This simple approach excluded physical parameters, such as mixing ratio and atmospheric profile, however, renders it generally suitable to compute potential intensity associated with TCs spatially and weakly temporally and performs well for stronger storms. A futuristic prediction by the HadCM3 climate model under doubled CO2 indicates stronger storm surface wind speeds and rising SST, especially in the Northern Hemisphere

The Indian Ocean Dipole: A Missing Link between El Niño Modokiand Tropical Cyclone Intensity in the North Indian Ocean

This study is set out to understand the impact of El Niño Modoki and the Tropical Cyclone Potential Intensity (TCPI) in the North Indian Ocean. We also hypothesized and tested if the Indian Ocean Dipole (IOD) reveals a likely connection between the two phenomena. An advanced mathematical tool namely the Empirical Mode Decomposition (EMD) is employed for the analysis. A major advantage of using EMD is its adaptability approach to deal with the non-linear and non-stationary signals which are similar to the signals used in this study and are also common in both atmospheric and oceanic sciences. This study has identified IOD as a likely missing link to explain the connection between El Niño Modoki and TCPI. This lays the groundwork for future research into this connection and its possible applications in meteorology

Maximizing the Information Content of Ill-Posed Space-Based Measurements Using Deterministic Inverse Method

For several decades, operational retrievals from spaceborne hyperspectral infrared sounders have been dominated by stochastic approaches where many ambiguities are pervasive. One major drawback of such methods is their reliance on treating error as definitive information to the retrieval scheme. To overcome this drawback and obtain consistently unambiguous retrievals, we applied another approach from the class of deterministic inverse methods, namely regularized total least squares (RTLS). As a case study, simultaneous simulated retrieval of ozone (O3) profile and surface temperature (ST) for two different instruments, Cross-track Infrared Sounder (CrIS) and Tropospheric Emission Spectrometer (TES), are considered. To gain further confidence in our approach for real-world situations, a set of ozonesonde profile data are also used in this study. The role of simulation-based comparative assessment of algorithms before application on remotely sensed measurements is pivotal. Under identical simulation settings, RTLS results are compared to those of stochastic optimal estimation method (OEM), a very popular method for hyperspectral retrievals despite its aforementioned fundamental drawback. Different tweaking of error covariances for improving the OEM results, used commonly in operations, are also investigated under a simulated environment. Although this work is an extension of our previous work for H2O profile retrievals, several new concepts are introduced in this study: (a) the information content analysis using sub-space analysis to understand ill-posed inversion in depth; (b) comparison of different sensors for same gas profile retrieval under identical conditions; (c) extended capability for simultaneous retrievals using two classes of variables; (d) additional stabilizer of Laplacian second derivative operator; and (e) the representation of results using a new metric called “information gain”. Our findings highlight issues with OEM, such as loss of information as compared to a priori knowledge after using measurements. On the other hand, RTLS can produce “information gain” of ~40–50% deterministically from the same set of measurements.https://doi.org/10.3390/rs1007099

Error Estimation of Pathfinder Version 5.3 Level-3C SST Using Extended Triple Collocation Analysis

Sea Surface Temperature (SST) is an essential climate variable (ECV) for monitoring the state and detecting changes in the climate. The concept of ECVs, developed by the Global Climate Observing System (GCOS) program of the World Meteorological Organization (WMO), has been broadly adopted in worldwide science and policy circles Besides being a climate change indicator, the global SST field is an essential input for atmospheric models, air-sea exchange studies, understanding marine ecosystems, operational weather, and ocean forecasting, military and defense operations, tourism, and fisheries research. It is, therefore, critical to understand the errors associated with SST measurements from both in situ measurements and satellite observations. The customary way of validating a satellite SST is to compare it with in situ measured SSTs. This method, however, will have inaccuracies due to uncertainties involving both types of measurements. A triple collocation (TC) error analysis can be implemented on three mutually independent error-prone measurements to estimate the root-mean-square error (RMSE) of each measurement. In this study, the error characterization for the Pathfinder SST version 5.3 (PF53) dataset is performed using an extended TC (ETC) method and reported to be in the range of 0.31 to 0.37 K. These values are reasonable, as is evident from corresponding very high (~0.98) unbiased signal-to-noise ratio (SNR) values

Group for High Resolution Sea Surface Temperature (GHRSST) analysis fields inter-comparisons—Part 2: Near real time web-based level 4 SST Quality Monitor (L4-SQUAM)

There are a growing number of level 4 (L4; gap-free gridded) sea surface temperature (SST) products generated by blending SST data from various sources which are available for use in a wide variety of operational and scientific applications. In most cases, each product has been developed for a specific user community with specific requirements guiding the design of the product. Consequently differences between products are implicit. In addition, anomalous atmospheric conditions, satellite operations and production anomalies may occur which can introduce additional differences. This paper describes a new web-based system called the L4 SST Quality Monitor (L4-SQUAM) developed to monitor the quality of L4 SST products. L4-SQUAM intercompares thirteen L4 products with 1-day latency in an operational environment serving the needs of both L4 SST product users and producers. Relative differences between products are computed and visualized using maps, histograms, time series plots and Hovmöller diagrams, for all combinations of products. In addition, products are compared to quality controlled in situ SST data (available from the in situ SST Quality Monitor, iQUAM, companion system) in a consistent manner. A full history of products statistics is retained in L4-SQUAM for time series analysis. L4-SQUAM complements the two other Group for High Resolution SST (GHRSST) tools, the GHRSST Multi Product Ensemble (GMPE) and the High Resolution Diagnostic Data Set (HRDDS) systems, documented in part 1 of this paper and elsewhere, respectively. Our results reveal significant differences between SST products in coastal and open ocean areas. Differences of >2 °C are often observed at high latitudes partly due to different treatment of the sea-ice transition zone. Thus when an ice flag is available, the intercomparisons are performed in two ways: including and excluding ice-flagged grid points. Such differences are significant and call for a community effort to understand their root cause and ensure consistency between SST products. Future work focuses on including the remaining daily L4 SST products, accommodating for newer L4 SSTs which resolve the diurnal variability and evaluating retrospectively regenerated L4 SSTs to support satellite data reprocessing efforts aimed at generating improved SST Climate Data Records