Presented by

Brian Mapes
Professor, Department of Atmospheric Sciences
Director, Meteorology and Physical Oceanography Program
RSMAS, University of Miami

Abstract:

Air is transparent to visible light, but the layer up to about 14km high cools by emitting infrared radiation. Gravity therefore creates mechanical energy to drive air motions (convection), in order to carry energy up there from the sun-warmed surface. But this purpose (telos, in Greek) doesn’t totally determine the form and scales and timing of convection. These are left to chance, further subject to the twitchy amplifying effect of water vapor condensation. The equator to pole sunlight gradient gives teleological purpose to some planet-spanning large scales (slant-wise convection in mid latitudes), so those are part of the mix. Surface features like continents and beautiful island chains imprint themselves in the sky. But many other kinds of motions have no apparent purpose — except perhaps to thwart our hopes of prediction, by scrambling each other! Sometimes, when telos is absent, nature maximizes randomness as a principle (or at least a useful assumption, turning our ignorance into a principle). But even that fails us in the atmosphere, because those twitchy amplifying feedbacks have subtly systematic aspects that introduce long threads of memory and history and coherent structures in space. Turbulence is a discouraging subject!

To focus discussion, I will emphasize precipitating deep tropical convection within the almost-uniformly warm tropics, or (for more rigor) in strictly uniform idealized “tropics-world” simulations. After a spectral kinetic energy equation is implanted in listener minds, this talk surveys some of the many conditional biases in the probability of development of convection’s unit structure (“cells”), and what those imply about the many scales of motion driven by convection’s gravitational work. Of course, all the field’s familiar named phenomena will be mentioned among those flywheels and splashes, but I hope these are illuminated a bit differently by this context, for experts to ponder once again and students to approach freshly.

Presented by

Professor Rong Fu
Department of Atmospheric and Oceanic Sciences
University of California, Los Angeles

Presented by

Dr. Sen Zhao
Postdoctoral Fellow
Department of Atmospheric Sciences
School of Ocean and Earth Science and Technology
University of Hawai’i at Mānoa

Presented by

Professor Yoo-Geun Ham
Department of Oceanography
Chonnam National University, South Korea

August 25, 2021 at 3:30PM HST

Presented by

Dr. Fei-Fei Jinn
Professor
Department of Atmospheric Sciences
University of Hawaii

August 25, 2021 at 3:30PM HST

Presented by

Dr. Kao-Shen Chung
Assistant Professor
Department of Atmospheric Sciences
National Central University, Taiwan

April 21, 2021 at 3:30PM HST

To view the recording, please click above or follow this link:

http://www.soest.hawaii.edu/met/seminar_recordings/ATMO 765 Seminar presented by Kao-Shen Chung 20210421.mp4

Abstract:

In this study, by using composite radar data from Central Weather Bureau (CWB), 16 typhoons are selected to examine the performance of the McGill Algorithm for Precipitation nowcasting using Lagrangian Extrapolation (MAPLE) over Taiwan area. In addition, instead of blending the precipitation between radar extrapolation and numerical model, information of wind is blended to improve the nowcasting system. It is found that the hybrid system could capture and maintain the circulation of rotation and rain-band structure much better than the original system. To validate the performance of nowcast, continuous, categorical, and neighborhood method (FSS score) are applied for verifications. For 16 typhoon cases, results of radar extrapolation show the quantitative precipitation nowcasting could last at least 2 hours. When blending the wind information from numerical model, it is able to improve the performance of nowcast for another 1 hour, which extends the capability of nowcast up to 3 hours. Furthermore, it is found the hybrid system performs better after typhoon landed over Taiwan even though orographic effect has to be considered.

April 14, 2021 at 3:30PM HST

To view the recording, please follow this link:

http://www.soest.hawaii.edu/met/seminar_recordings/ATMO 765 Seminar presented by Yi-Chi Wang 20210414.mp4

Abstract:

This study evaluated the performance of the Taiwan Earth System Model version 1 (TaiESM1) in simulating the observed climate variability in the historical simulation of the Coupled Model Intercomparison phase 6 (CMIP6). TaiESM1 was developed based on the Community Earth System Model version 1.2.2, with the inclusion of several new physical schemes and improvements in the atmosphere model. The new additions include an improved triggering function in the cumulus convection scheme, a revised distribution-based formula in the cloud fraction scheme, a new aerosol scheme, and a unique scheme for three-dimensional surface absorption of shortwave radiation that accounts for the influence of complex terrains. In contrast to most model evaluation processes, which focus mainly on the climatological mean, this evaluation focuses on climate variability parameters, including the diurnal rainfall cycle, precipitation extremes, synoptic eddy activity, intraseasonal fluctuation, monsoon evolution, and interannual and multidecadal atmospheric and oceanic teleconnection patterns. A series of intercomparison between the simulations of TaiESM1 and CMIP6 models and observations indicate that TaiESM1, a participating model in CMIP6, can realistically simulate the observed climate variability at various time scales and performs better than the other CMIP6 models in terms of many key climate features.

The MIX program offers UHM students an opportunity to study abroad for a summer, semester, or academic year to enrich their academic experiences.

They hope to encourage more students to study abroad with their program. The link below is a recording of a presentation given to the ATMO Seminar audience on March 31st, 2021. It covers:

• Programs by Country
• How to Apply
• Financial Aid

Presented by

Professor Shu-Chih Yang
Department of Atmospheric Sciences
National Central University, Taiwan

March 10, 2021 at 3:30PM HST

To view the recording, please follow this link:

http://www.soest.hawaii.edu/met/seminar_recordings/ATMO 765 Seminar presented by Shu-Chih Yang 20210310.mp4

Abstract:

In conventional data assimilation, high-resolution data is often re-sampled with strategies like superobbing or data thinning to fulfill the assumption of uncorrelated observation errors. This also sacrifices the advantage of high spatial resolution observations that can provide essential detailed structures, such as the intensification of the convection. However, assimilating the high-resolution data without considering the observation error correlation can lead to overfitting and thus degrade the performance of data assimilation and forecast. This study proposes a strategy to include the observation error correlation for assimilating the radar radial velocity under the framework of the WRF Local Ensemble Transform Kalman Filter Radar Assimilation System. The impact is investigated based on a short-term precipitation prediction of the heavy rainfall case on 2nd June 2017 in Taiwan.

The introduction of correlated observation error for radar radial winds exhibits more small-scale features in the wind analysis corrections compared to the experiment using the independent observation assumption. The modification on wind corrections leads to stronger convergence accompanied by higher water vapor content, and enhances local convections, resulting in more accurate simulated reflectivity. Consequently, these modifications lead to a better forecast in terms of reflectivity, precipitation and probability quantitative precipitation forecast (PQPF).