Browsing by Author "Huda, Md Nurul"

Now showing 1 - 2 of 2
  • Thumbnail Image
    3D Simulator for Wind Interferometer Data-Model Comparison
    Huda, Md Nurul (Virginia Tech, 2019-09-27)
    The connection between earth and space weather has numerous impacts on spacecraft, radio communications and GPS signals. Thus, predicted & modeling this region is important, yet models (both empirical and first principles) do a poor job of characterizing the variability of this region. One of the main objectives of the NASA ICON mission is to measure the variability of the ionosphere and thermosphere at low-mid latitudes. The MIGHTI instrument on ICON is a Doppler Interferometer that measures the horizontal wind speed and direction with 2 discrete MIGHTI units, separated by 90˚, mounted on the ICON Payload Interface Plate. This work focuses on building a simulation of wind interferometer data, similar to MIGHTI, using a first-principles model as the input dataset, which will be used for early validation and comparison to the MIGHTI data. Using a ray-tracing approach, parameters like O, O2, O+, O2+, T, wind, solar F10.7 index will be read for every point along every ray from the model and brightness and Line of Sight (LOS) wind will be calculated as functions of altitude and time. These data will be compared to the MIGHTI observations to both to establish the limitation of such models, and to validate the ICON data. ICON will help determine the physics of our space environment and pave the way for mitigating its effects on our technology, communications systems and society. However, ICON is yet to launch and due to the unavailability of MIGHTI data, we have selected another instrument called WINDII (Wind Imaging Interferometer) from a different mission UARS (Upper Atmosphere Research Satellite) to demonstrate the utility of this data-model comparison. Similar to MIGHTI, WINDII measures Doppler shifts from a suite of visible region airglow and measures zonal and meridian winds, temperature, and VER (Volume Emission rate) in the upper mesosphere and lower thermosphere (80 to 300 km) from observations of the Earth's airglow. We will use a similar approach discussed for MIGHTI to calculate vertical profile of Redline airglow, Wind velocity, emission rate and compare them with our simulated results to validate our algorithm. We initially thought asymmetry calculation along the Line of Sight (LOS) would be the limiting factor. We believe there are other things going on such as variability in the winds associated with natural fluctuations in the thermosphere, atmospheric waves, inputs from the sun and the atmosphere below etc., appear to be bigger factor than just asymmetry along the line of sight.
  • Thumbnail Image
    Machine Learning for Improvement of Ocean Data Resolution for Weather Forecasting and Climatological Research
    Huda, Md Nurul (Virginia Tech, 2023-10-18)
    Severe weather events like hurricanes and tornadoes pose major risks globally, underscoring the critical need for accurate forecasts to mitigate impacts. While advanced computational capabilities and climate models have improved predictions, lack of high-resolution initial conditions still limits forecast accuracy. The Atlantic's "Hurricane Alley" region sees most storms arise, thus needing robust in-situ ocean data plus atmospheric profiles to enable precise hurricane tracking and intensity forecasts. Examining satellite datasets reveals radio occultation (RO) provides the most accurate 5-25 km altitude atmospheric measurements. However, below 5 km accuracy remains insufficient over oceans versus land areas. Some recent benchmark study e.g. Patil Iiyama (2022), and Wei Guan (2022) in their work proposed the use of deep learning models for sea surface temperature (SST) prediction in the Tohoku region with very low errors ranging from 0.35°C to 0.75°C and the root-mean-square error increases from 0.27°C to 0.53°C over the over the China seas respectively. The approach we have developed remains unparalleled in its domain as of this date. This research is divided into two parts and aims to develop a data driven satellite-informed machine learning system to combine high-quality but sparse in-situ ocean data with more readily available low-quality satellite data. In the first part of the work, a novel data-driven satellite-informed machine learning algorithm was implemented that combines High-Quality/Low-Coverage in-situ point ocean data (e.g. ARGO Floats) and Low-Quality/High-Coverage Satellite ocean Data (e.g. HYCOM, MODIS-Aqua, G-COM) and generated high resolution data with a RMSE of 0.58◦C over the Atlantic Ocean.The second part of the work a novel GNN algorithm was implemented on the Gulf of Mexico and showed it can successfully capture the complex interactions between the ocean and mimic the path of a ARGO floats with a RMSE of 1.40◦C.