DMSP-SSM/I retrieval of proper surface winds during monsoon depression

Recently developed various global microwave algorithms for DMSP-SSM/I satellite data are used for the estimation of surface winds over the Indian ocean. Sea surface wind speeds from these algorithms are compared with sea surface wind speeds reported by coincidental Minicoy island (lowest height 2 m a.s.l.) station over the Arabian sea. A statistical comparison of these algorithms is made in terms of rms error, correlation coefficient, bias and standard deviation. Algorithm of Petty showed best results in the comparison. On the basis of this algorithm a notable characteristic feature such as acquiring of large area of strong surface winds (12-15 ms-1) to the south of dipping of monsoon trough in head Bay and then encircling of these winds during further development of low and depression (22-27 July 1992) is observed. This complete life cycle monitoring assessment of monsoon depression in respect of surface winds based on DMSP-SSM/I satellite data encourages to utilise our IRS-P4 (Oceansat-1) satellite data at different frequencies to emerge more details of various weather systems over the Indian region.

Areal Precipitation Coverage Ratio for Enhanced AI Modelling of Monthly Runoff: A New Satellite Data-Driven Scheme for Semi-Arid Mountainous Climate

Satellite remote sensing provides useful gridded data for the conceptual modelling of hydrological processes such as precipitation–runoff relationship. Structurally flexible and computationally advanced AI-assisted data-driven (DD) models foster these applications. However, without linking concepts between variables from many grids, the DD models can be too large to be calibrated efficiently. Therefore, effectively formulized, collective input variables and robust verification of the calibrated models are desired to leverage satellite data for the strategic DD modelling of catchment runoff. This study formulates new satellite-based input variables, namely, catchment- and event-specific areal precipitation coverage ratios (CCOVs and ECOVs, respectively) from the Global Precipitation Mission (GPM) and evaluates their usefulness for monthly runoff modelling from five mountainous Karkheh sub-catchments of 5000–43,000 km2 size in west Iran. Accordingly, 12 different input combinations from GPM and MODIS products were introduced to a generalized deep learning scheme using artificial neural networks (ANNs). Using an adjusted five-fold cross-validation process, 420 different ANN configurations per fold choice and 10 different random initial parameterizations per configuration were tested. Runoff estimates from five hybrid models, each an average of six top-ranked ANNs based on six statistical criteria in calibration, indicated obvious improvements for all sub-catchments using the new variables. Particularly, ECOVs were most efficient for the most challenging sub-catchment, Kashkan, having the highest spacetime precipitation variability. However, better performance criteria were found for sub-catchments with lower precipitation variability. The modelling performance for Kashkan indicated a higher dependency on data partitioning, suggesting that long-term data representativity is important for modelling reliability.

Utilizing Multi-Temporal Thermal Data To Assess Environmental Land Degradation Impacts: Example From Suez Canal Region, Egypt.

Land surface temperature (LST) analysis of Satellite data is critical for studying the impacts of geo-environmental, hydrometeorological, and land degradation. However, challenges arise to resolve the LST and ground field data resulting from the constant development of land use and land cover (LULC). This study aims to monitor, analyze, assess, and map the environmental land degradation impacts utilizing image processing and GIS tools of space-borne thermal data and fieldwork. Two thermal and optical sets of multi-temporal Landsat TM+5 and TIRS+8 satellite data dated 1984 and 2018 were used to test, detect, and map the thermal and LULC change and their land degradation in the Suez Canal region (SCR). The LULC classification was categorized into seven classes: water bodies, urban, agricultural land, barren land, wetland, clay, and salt crust. LULC and LST change detection and mapping results revealed that the impervious surface, industrial area, saline soil, and urban area have high LST, while wetlands, vegetation cover, and water bodies suffered low LST. The spectral, LST profiles and statistical analyses examined the association between LST and LULC deriving factors. The cluster analyses defined the relationship between LST and LC patterns at the LU level, where the fast transformation of LULC had significant changes in LST. According to these analyses and the fieldwork observations, the SCR was divided into six main areas. These areas vary in LST in association with land degradation and hydro-environmental impacts such as rising groundwater levels, salt accumulation, active seismic fault zones, water pollution, and urban and agricultural activities.

The study of a severe dust storm over Persian Gulf, the Gulf of Oman and parts of the Indian Ocean with two operational dust forecasting models

Introduction: The entry of dust particles into water areas, which has increased sharply in recent years, causes a lot of environmental damage. The Persian Gulf and the Gulf of Oman are among the water areas that are covered with dust many times of the year. Materials and methods: In this study, a severe dust from July 27 to 31, 2018 is analyzed, in which a large part of the Persian Gulf, Oman Sea and the western part of the Indian Ocean was involved. To study this phenomenon from different perspectives, satellite products, visibility from synoptic stations and synoptic maps were analyzed and the output of two numerical dust models of NASA-GEOS and DREAM8-MACC were examined. To qualitative and quantitative evaluate of the model outputs, the Aerosol Optical Depth (AOD) of TERRA/MODIS was used. Results: Satellite imagery shows that in this case study, parts of the Persian Gulf and the Sea of Oman were affected by dust, and on July 30, dust particles entered the western half of the Indian Ocean. Comparison of model outputs with satellite data resulted that both models underestimate the AOD values, especially over water, and do not show well the entrance of dust particles into the eastern part of the Persian Gulf, the Gulf of Oman and the western half of the Indian Ocean. Conclusion: Qualitative and quantitative comparison of AOD output of the two models with satellite data showed that the NASA-GEOS model had better performance and its output correlation with observational data was higher.