Presented on February 22, 2023, by

Dr. Paolo Antonelli, PhD
Co-Founder and Director
Adaptive Meteo

Department of Atmospheric Sciences

Feb 22, 2023, 8:42 AM (12 days ago)

to Department, bcc: mike.gonsalves
You are invited to our weekly hybrid (in-person and online) Atmospheric Sciences Spring 2023 seminar.

When: Wednesday, February 22, 2023 at 3:30PM HST
Where: MSB 100 (Marine Sciences Building, UH Manoa Campus) and Zoom

There will be refreshments, tea and cookies on the MSB Lanai starting at 3 PM; a good opportunity to network and brainstorm about research prior to the seminar.

Zoom Invitation Link: https://hawaii.zoom.us/j/94467712655
Meeting ID: 944 6771 2655
Passcode: 833070

Title: Hyperspectral Infrared (IR) Remote Sensing of the Atmosphere

Dr. Paolo Antonelli, PhD
Co-Founder and Director
Adaptive Meteo

Presentation short abstract: Hyperspectral Infrared (IR) remote sensing of the atmosphere involves the use of advanced sensors such as IASI and CrIS to collect data about the atmosphere in a large number of spectral bands. These sensors detect radiation in the IR part of the spectrum, which is absorbed and emitted by gases and particles in the atmosphere. By analysing the unique spectral signature of each atmospheric component, scientists can gain a better understanding of the composition and structure of the atmosphere. This information can be used to characterise the Earth’s atmospheric composition and dynamics. The ability of hyperspectral IR remote sensing technology to improve our understanding of the atmosphere has significant impacts in the fields of meteorology and climatology but it has also the potential to benefit other fields such as astronomy by mitigating the noise introduced by the Earth’s atmosphere. The presentation will cover the basics of hyperspecral remote sensing, current and future spaceborne sensors, and some of current and potential applications.

Short Bio: Paolo Antonelli (PhD) received his Ph.D. in Atmospheric and Oceanic Sciences from the University of Wisconsin – Madison in 2001 under the guidance of Prof. William L. Smith. His research focused on IR remote sensing of the Earth at high spectral resolution, and his dissertation work dealt with the application of Principal Component Analysis for data compression and data inversion. For about 15 years, Dr. Antonelli has been a research scientist at the University of Wisconsin-Madison Space Science and Engineering Center. In 2004/2005 Dr. Antonelli worked as coordinator of the Remote Sensing activities at the Mediterranean Agency for Remote Sensing and environmental control (MARSec) in Benevento Italy. Member of the MTG-IRS Advisory Group from 2014 to 2018, he has collaborated with EUMETSAT in the framework of MTG-IRS development, for the development of an High Performance Level 2 Validation and Demonstration Prototype, and for the development of a hyperspectral Level 2 Data Assimilation system. Since August 2017, Dr. Antonelli co-founded and directs Adaptive Meteo, an Italian start-up focused on innovative use of satellite data into Numerical Weather Prediction models and from 2018 he has been collaborating with the University of Hawaii in the field of IR hyperspectral Data Assimilation over the Pacific and over the Arctic.

Presented on February 15, 2023, by

DR. STEVEN BUSINGER, Ph.D., C.C.M.
Department of Atmospheric Sciences
University of Hawaii

Abstract:

This seminar will explore the mechanics of preparing and presenting an effective conference talk or longer seminar. It is designed for anyone who would like to improve their speaking ability and style.

Presented on Wednesday, February 8, 2023, by

Michael M. Bell
Colorado State University

Abstract:

Weather radar has advanced scientific progress in many areas of atmospheric science, such as
tropical and mid-latitude weather prediction, regional climate and climate change, cloud
microphysics, dynamics of convective storms, and extreme weather impacts. Many radar datasets
are publicly available, as well as free, open-source community software tools such as the Lidar
Radar Open Software Environment (LROSE) developed jointly by Colorado State University
(CSU) and the National Center for Atmospheric Research (NCAR). There are also several radars
that are requestable through the National Science Foundation, including the CSU Sea-Going
Polarimetric (SEA-POL) radar which has recently been designated as a community facility
available for deployment requests. The stabilization and rugged design of SEA-POL allow for
research-grade accuracy of polarimetric weather radar measurements on a ship or on land that
can probe the structure of clouds and precipitation. SEA-POL and other radars, including
airborne and ground-based platforms, can also contribute to interdisciplinary science in
oceanography, hydrology, and water resources. In this presentation we will provide an overview
of some recent advancements in tropical weather using radar, including new insights into tropical
cyclone rapid intensification, cloud microphysics, and oceanic precipitation. We will also present
an overview of SEA-POL and some highlights from past deployments, along with information
for potential future users of this community facility.

Presented on January 25, 2023, by

Francis Fujioka
USDA Forest Service
Fire Meteorology Research Project Leader (Retired)
Riverside Fire Lab, Riverside, California

Abstract:

Faced with an increasing threat of wildfires, the Hawai’i State Division of Forestryrequested the assistance of the USDA Forest Service in setting up a customized version of the National Fire Danger Rating System more than 50 years ago. This seminar covers a brief history of the subsequent fire science that evolved in the period from the 1970s through 2010 in service of state, federal and local fire management in Hawai’i. The importance of weather in the context of the fire spread model underlying fire danger and fire behavior analysis and prediction will be covered. Presently, the fire danger prediction system that was developed for the state is not in use. The operational challenges of implementing this technology will be discussed.

Presented on January 11, 2023, by
Professor Daehyun Kim
Department of Atmospheric Sciences, Univ. of Washington

Abstract:

The Madden-Julian Oscillation (MJO) is the dominant mode of tropical intraseasonal variability that interacts with many other Earth system phenomena, including high-impact weather events in the midlatitude. The prediction skill of the MJO in many operational models is lower than potential predictability, partly because our understanding of the MJO’s predictability is limited.

In this study, we investigate the source of the MJO’s predictability by combining a machine learning (ML) technique with a 1200-year-long simulation made with Community Earth System Model version 2 (CESM2). A convolutional neural networks (CNN)-based MJO prediction model is first trained using the CESM2 simulation data and then fine-tuned using observational data via the transfer learning, with five 2-D fields of atmospheric variables as input and the real-time multivariate MJO indices as the output. The source of MJO predictability in the CNN model is examined via explainable artificial intelligence (XAI) methods that quantify the relative importance of the input variable.
Our CNN model outperforms previous statistical models and many operational forecasts with the prediction skill of about 25 days. Applying the XAI methods to the CNN model highlights precipitable water anomalies over the Indo-Pacific warm pool as key precursors of the subsequent MJO development for 1-3 weeks forecast lead times. Surface temperature anomalies are also found to play an important role, especially for longer (> 3 weeks) forecast lead times. Our results suggest a realistic representation of moisture dynamics is crucial for accurate MJO prediction.

Presented on November 16, 2022, by Professor Masa Watanabe from Atmosphere and Ocean Research Institute University of Tokyo.

Abstract:

Equilibrium climate sensitivity (ECS) – global-mean surface temperature response to a doubling of the atmospheric CO2 concentration- is a primary measure of global warming. ECS was first numerically calculated by Suki Manabe and his collaborators in their pioneering work. It is not only the metric of physical climate change but also crucial in estimating remaining carbon budget and hence the mitigation policy making. The estimate of ECS remains a challenge in climate sciences for more than 40 years, partly arising from difficulty in understanding climate feedback mechanisms. The situation, however, has changed during the last decade when the climate science community joined forces to re-assess ECS based on multiple lines of evidence. This effort resulted in the IPCC AR6 that reduced uncertainty in the ECS range to half the previous assessments. I overview the history of ECS assessment in the first half of the talk, followed by our ongoing work to better estimate the future of global temperature change in realistic emission scenarios, premised on the assessed ECS range, by constraining the so-called pattern effect on climate feedbacks, which is a hot topic in the community.

Presented on November 2, 2022, by

Professor Róisín Commane
Department of Atmospheric Sciences
Dept. of Earth & Environmental Sciences,
Lamont-Doherty Earth Observatory,
Columbia University

Abstract:

Carbon dioxide (CO2) and methane are increasing rapidly in the atmosphere and both have a strong inter-hemispheric gradient driven by non-uniform surface-atmosphere fluxes. The Arctic is warming at twice the global average and the carbon-rich permafrost soils of northern high-latitude ecosystems have the capacity to release large amounts of carbon to the atmosphere. Historically, the focus has been on carbon uptake in summer, when ample light and good weather make airborne and remote sensing measurements possible. However, our recent work has shown that CO2 and CH4 emissions in early winter are increasing, in line with increasing winter-time temperatures, so care must be taken to consider all seasons
when calculating annual carbon budgets. Arctic ecosystems are responding to the warming driven by anthropogenic carbon emissions. If we want to reduce Arctic warming, we have to reduce carbon emission in lower latitudes, especially cities. Many city governments in the US have committed to reducing their emissions of GHGs based on inventories calculated for the city. However, few cities measure their carbon emissions to know if the enacted policies are having the desired effect. The New York Metro Area (pop. 20M) is the most populous urban area in the United States (US) and the largest urban source of CO 2 in the US. The region also has some of the worst summertime air quality outside of California. In response to the COVID-19 pandemic, New York ordered state-wide closures of all non-essential businesses in March 2020. On-road transportation and economic activity were dramatically reduced in the New York City metro area. Fortuitously, we began measuring CO2, CH4 and CO at an observatory in Manhattan in January 2020. I will present a preliminary analysis of the changes we have seen in the atmospheric composition of New York City and discuss the possible causes of these changes.

The presenter’s microphone was muted for the first 12 minutes of the recording. We apologize for the inconvenience.

Presented on September 21, 2022, by

Dr. Shaocheng Xie
Research Scientist, Group Leader
Cloud Processes Research and Modeling
Atmospheric, Earth & Energy Division (L103)
Lawrence Livermore National Laboratory

Abstract:

Dominant summertime disturbances along the subtropical Meiyu front are eastward-propagating synoptic-scale waves that are coupled with precipitation and moisture under a moderate background vertical shear. To what extent the intensity and structure of such synoptic disturbances change under global warming is investigated by diagnosing 18 models from Phase 6 of the Coupled Model Intercomparison Project. The model diagnosis reveals that there is a robust increase in the intensity of synoptic-scale activity along the Meiyu front, while the wavelength and phase speed remain unchanged. The cause of such changes of the synoptic-scale variability in the future warmer climate is investigated through the analysis of a moist baroclinic instability model framework. It is found that the growth rate of the most unstable mode strengthens in the future warmer climate, while the zonal wavenumber and phase speed of the most unstable mode remain unchanged, which is consistent with the CMIP6 future projections. The enhanced synoptic-scale variability is primarily attributed to the increase of background meridional and vertical moisture gradients under a warmer climate through strengthened positive moisture-convection-circulation feedback, while the changes of background vertical shear and convective adjustment times are insignificant.