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Figure 1. Biases of accumulated precipitation (mm) relative to the MRMS ground-based analysis of the five machine learning models studied during the period of 1 May to 30 September, 2022. Biases of the operational MiRS algorithm are also shown in the bottom right panel.

Using Machine Learning to Improve Microwave-Based Precipitation Estimates

ESSIC/CISESS scientist Chris Grassotti along with CIRA and NOAA researchers Shuyan Liu and Quanhua (Mark) Liu, recently published a paper in the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing titled “Warm-Season Microwave Integrated Retrieval System (MiRS) Precipitation Improvement Using Machine Learning Methods”.

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Machine Learning-Based Estimation of Tropical Cyclone Intensity from Advanced Technology Microwave Sounder Using a U-Net Algorithm

ESSIC/CISESS scientists Yong-Keun Lee and Christopher Grassotti are co-authors on a new paper in Remote Sensing led by first author Zichao Liang, a student who interned with the MiRS team during the summer of 2023. NOAA scientists Lin Lin and Quanhua Liu also co-authored the paper. The paper, titled “Machine Learning-Based Estimation of Tropical Cyclone Intensity from Advanced Technology Microwave Sounder Using a U-Net Algorithm”, assesses the use of the U-Net model to estimate surface wind speed and surface pressure over pure ocean conditions.

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Figure: Field experiments with the RHG-BRDF system over grass, soil and sand scenes and its calibration with a reflectance reference board.

CISESS Seed Grant Project Develops a Robotic RHG-BRDF Measurement System

During its one-year funding period, this CISESS Seed Grant project expanded the work of the student-oriented CISESS Remote Sensing Laboratory by building equipment for post-launch radiometric validation using in situ measurements of reflective solar band calibration. ESSIC/CISESS Scientist Xi Shao, along with Sirish Uprety, Tung-Chang Liu, and Xin Jin, developed a Robotic Hyperspectral Ground Bi-directional Reflectance Distribution Function (RHG-BRDF) measurement system. Once built, they worked with three undergraduate students to perform field hyperspectral measurements of different ground targets. The student also developed python modules for converting measurements to hyperseptral reflectance, data visualization and analysis.

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Figure 1. The local perturbations in observed microwave brightness temperatures from an ascending orbit of (a) MetOp-B AMSU-A channel 14, (b) MetOp-C AMSU-A channel 14, a descending orbit of (c) NOAA-20 ATMS channel 15, and (d) SNPP ATMS channel 15 on January 15, 2022. The black triangle at the center for each panel is the Tonga volcano location. The outermost black-curved lines from the Tonga volcano location correspond to a phase speed of 330 m/s assuming that the perturbation has been generated at the time and location of initial volcanic eruption. From the 2nd outermost black-curved lines to the innermost lines, the phase speeds are 300, 270, and 230 m/s, respectively. The time information in each panel indicates the approximate observation time for the Lamb wave (between 300 m/s and 330 m/s indicated by black right-pointing triangles) and for the lead gravity wave (between 230 m/s and 270 m/s indicated by red right-pointing triangles). Red dots indicate the pixels where the brightness temperature perturbation is larger than 1.2 K.

Satellite Microwave Observations of the Hunga Tonga Eruption’s Atmospheric Waves

ESSIC/CISESS scientists Yong-Keun Lee and Christopher Grassotti are authors on a new paper in Geophysical Research Letters describing the first attempt to perform a detailed analysis of the stratospheric impact of the eruption from satellite microwave observations. The other authors on the paper are Neil Hindley from University of Bath and Quanhua (Mark) Liu from NOAA’s Center for Satellite Applications and Research.

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The logo of the CTR Wilson meeting

Zhang Presents Her New Book at the CTR Wilson Meeting

ESSIC/CISESS scientist Daile Zhang and her coauthor Ron Holle virtually presented their new
book–Flashes of Brilliance: The Science and Wonder of Arizona Lightning –at the CTR Wilson
meeting on November 16, 2023. They discussed the motivation of writing the book and
introduced the content of each chapter. The audiences were interested in creating
undergraduate level courses and materials based on the book.

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Figure: The International Space Station Lightning Imaging Sensors 128 X 128-pixel array of (a) event count, (b) total event energy density, (c) mean event energy density, (d) pixel minimum energy density, (e) pixel maximum energy density, and (f) pixelwise 95% quantile energy density during March 2017–September 2020, computed separately for each pixel, indexed by CCD pixel numbers.

Evaluating Lightning Observations from Space

ESSIC/CISESS Scientists Daile Zhang, Scott Rudlosky (NOAA), and colleagues published a study that uses the well-documented Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensors (LIS) performance to determine if the International Space Station (ISS) LIS performs well enough to bridge the gap between TRMM LIS and the new generation of Geostationary Lightning Mappers (GLMs).

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Building & Deploying Tools to Better Observe Lightning in the Washington D.C. Region & Beyond

In January 2022, ESSIC/CISESS Scientist Daile Zhang won a CISESS Seed Grant to evaluate NOAA’s Geostationary Lightning Mappers (GLMs) on the GOES-16 and -17 Satellites and the upgraded Mid-Atlantic Lightning Mapping Array (MALMA) using a network of low-cost and innovative atmospheric electricity and lightning measurement tools to take lightning videos. Recently, the initial Seeds Grant period ended and Zhang reported her results.

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