Proceedings Of The 18th Annual Meeting Of The Asia Oceania Geosciences Society (Aogs 2021)

The 18th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2021) was held from 1st to 6th August 2021. This proceedings volume includes selected extended abstracts from a challenging array of presentations at this conference. The AOGS Annual Meeting is a leading venue for professional interaction among researchers and practitioners, covering diverse disciplines of geosciences

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Estimation of monsoon rainfall by single polarization weather radar

Weather radar can offer synoptic measurement at a higher temporal and spatial resolution to extract the rain information. Rainfall can be inverted from the radar reflectivity using the power-law relation to ground rain gauge measurement. The relationship known as Z-R model has been established in many variants but the uncertainty from the sampling bias and the Z-R variability of single-polarization radar observation on monsoon rain becomes subject to research. This study reports a novel research framework to systematically estimate the monsoon rainfall using new Z-R model on the single-polarization weather radar in Kelantan. The sampling bias was quantified by the pixel matching procedure while the non-linear Levenberg Marquardt (LM) regression and the Artificial Neural Network (ANN) regression at different rain intensity and radar range were introduced to minimise the Spatio-temporal variability of the new Z-R model. This study uses 10-minute reflectivity data recorded in Kota Bahru radar station and hourly rain record at the nearby 58 gauge stations in 2013 to 2015. The three-dimensional nearest neighbour interpolation proves that the sampling bias can be quantified. The LM shows an improvement of about 12% if the spatial adjustment was applied in the regression. Unlike LM, the ANN is more robust and independent to the spatial adjustment thus it could provide more accurate and reliable monsoon rain information in heterogenous rainy condition. The ANN model provides accuracy of ± 0.4 mm/hr, ± 1.0 mm/hr and ± 8.2 mm/hr for low, medium and high rain intensity respectively with correlation coefficient > 0.7 (p 0.5 and accuracy improvement about 8 %, 10% and 5% for abovementioned rain intensity respectively. Radar derived rainfall maps present the rain distribution was more concentrated in all downstream but only covered 1/3 of the upstream in Kelantan rivers. Further research is needed before the technique could be applied to any single-polarization system in Southeast Asia to achieve better accuracy of rain information extraction

ASSESSMENT OF ONE-MOMENT AND TWO-MOMENT BULK MICROPHYSICS AND SPECTRAL BIN MICROPHYSICS SCHEMES USING IDEALIZED SUPERCELL SIMULATIONS AND REAL DATA CONVECTIVE-SCALE PREDICTIONS

Optimal hydrometeor parameterization and their associated processes in microphysics schemes (both spectral bin and bulk) continue to evolve as these schemes attempt to match observed hydrometeor complexity. This dissertation spans several flavors of microphysics schemes: the one-moment Unified Model (UM), the partially-two moment Thompson and Morrison with one rimed ice category, the two-moment Milbrandt-Yau (MY2) and National Severe Storms Laboratory (NSSL) with two rimed ice categories, the Predicted Particle Properties (P3) with multiple mass assumptions within its ice particle size distributions (PSDs), and the spectral bin Hebrew University Cloud Model (HUCM). Microphysical performance (including their bias documentation) is examined in idealized supercell simulations by considering cloud ice and snow moment (and their associated budget) evolution, cloud ice and snow PSDs, low-level classic polarimetric radar signatures (ZDR arc and hail signature in the forward flank downdraft), and ice hydrometeor contoured frequency by altitude diagrams (CFADs). Two test cases over the Korean Peninsula (Changma front and Typhoon Sanba [2012]) are compared to S-band radar observations by applying a dual-polarization radar variable simulator to UM output. 2018 NOAA Hazardous Weather Testbed (HWT) Spring Experiment seasonal forecasts over much of the continental United States (CONUS) and four select convective line cases are both quantitatively and qualitatively compared to observed composite reflectivity, accumulated precipitation, and brightness temperature in the 11.2 μm channel for short-term (t = 1 – 6 forecast hours) and next-day (t = 12 – 36 forecast hours) forecasts. UM microphysics struggles to match observed dual-pol variables because of its one-moment parameterization of rain, specifically its rain PSD intercept parameter N0 diagnosis. As N0 varies inversely with rain mass, the scheme is producing too many small (large) drops in regions of too weak (intense) reflectivity. Both the fully two-moment MY2 and NSSL schemes are able to simulate a local maximum of ZDR near the forward flank edge and a gradual decrease in the direction of the deep-layer storm relative mean wind vector, but the large, dry hail in the MY2 scheme reduces ZDR on the edge of the supercell, while the NSSL’s ZDR arc is less elongated compared to typical observations. The P3 scheme with two ice categories is unable to simulate either signature, due to the restrictive rain and ice PSD slope Λ limiters (both directly and indirectly) preventing larger particles. In idealized supercell simulations, the HUCM and NSSL schemes simulate larger ice crystal moments than snow, while the Thompson scheme simulates more snow mass. This is due to the aggressive cloud ice to snow conversion in the scheme, which is intended given the assumed snow PSD. The flexible spectral bin HUCM PSDs simulate less small snow particles than the rigid bulk NSSL and Thompson schemes, but also sediments too large snow to the surface. Over the 2018 NOAA HWT Spring Experiment, the Morrison scheme displays a large-scale storm structure (Z ≥ 15 dBZ) overprediction bias for short-term forecasts that lessens for next-day forecasts, and is likely due to enhanced horizontal graupel advection in the scheme because of its smaller fall speed. Both the NSSL and Thompson schemes underpredict this storm structure. The Morrison and NSSL schemes both overpredict convective storm structure (Z ≥ 40 dBZ), due to overadvected Morrison graupel melting to large rain, and the “large hail” category design in the NSSL scheme. Each BMP underpredicts light and heavy surface precipitation, indicating that the BMPs underpredict either total column mass and/or its sedimentation to the surface. The documented shortcomings and biases in this dissertation are essential to numerical modelers and their users alike, as users should select the appropriate scheme for their simulated storm, and numerical modelers can optimally tune/construct their microphysics scheme

Remote Sensing of Precipitation: Part II

Precipitation is a well-recognized pillar in the global water and energy balances. The accurate and timely understanding of its characteristics at the global, regional and local scales is indispensable for a clearer insight on the mechanisms underlying the Earth’s atmosphere-ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne. This volume hosts original research contributions on several aspects of remote sensing of precipitation, including applications which embrace the use of remote sensing in tackling issues such as precipitation estimation, seasonal characteristics of precipitation and frequency analysis, assessment of satellite precipitation products, storm prediction, rain microphysics and microstructure, and the comparison of satellite and numerical weather prediction precipitation products

Deep Learning Techniques in Extreme Weather Events: A Review

Extreme weather events pose significant challenges, thereby demanding techniques for accurate analysis and precise forecasting to mitigate its impact. In recent years, deep learning techniques have emerged as a promising approach for weather forecasting and understanding the dynamics of extreme weather events. This review aims to provide a comprehensive overview of the state-of-the-art deep learning in the field. We explore the utilization of deep learning architectures, across various aspects of weather prediction such as thunderstorm, lightning, precipitation, drought, heatwave, cold waves and tropical cyclones. We highlight the potential of deep learning, such as its ability to capture complex patterns and non-linear relationships. Additionally, we discuss the limitations of current approaches and highlight future directions for advancements in the field of meteorology. The insights gained from this systematic review are crucial for the scientific community to make informed decisions and mitigate the impacts of extreme weather events

Improving Flood Detection and Monitoring through Remote Sensing

As climate-change- and human-induced floods inflict increasing costs upon the planet, both in terms of lives and environmental damage, flood monitoring tools derived from remote sensing platforms have undergone improvements in their performance and capabilities in terms of spectral, spatial and temporal extents and resolutions. Such improvements raise new challenges connected to data analysis and interpretation, in terms of, e.g., effectively discerning the presence of floodwaters in different land-cover types and environmental conditions or refining the accuracy of detection algorithms. In this sense, high expectations are placed on new methods that integrate information obtained from multiple techniques, platforms, sensors, bands and acquisition times. Moreover, the assessment of such techniques strongly benefits from collaboration with hydrological and/or hydraulic modeling of the evolution of flood events. The aim of this Special Issue is to provide an overview of recent advancements in the state of the art of flood monitoring methods and techniques derived from remotely sensed data