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Tang, Q.

Publications and source records attributed to Tang, Q..

15 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Multiscale Wildfire Simulation Framework and Remote Sensing

Wildfire as one type of climate extreme events causes huge socioeconomic losses and damages. Large wildfires (i.e., generated pyrocumulonimbus (PyroCb)) can inject tremendous amounts of smoke into the stratosphere, where black carbon and organic carbon aerosols can persist months to years and influence climate by imposing a significant reduction in the radiative forcing like that associated with large volcanic eruptions or proposed via climate interventions such as geoengineering. Both observations and numerical modeling results clearly indicate an increasing trend in wildfire frequency and intensity in many regions during the recent decades with climate change. However, current understanding of wildfire remains largely uncertain owing to limitations of modeling capabilities in representing the multiscale wildfire physics and dynamics and a scarcity of observations constraining important wildfire and environmental variables. This study primarily aims to improve the wildfire simulation capabilities in the state-of-the-art climate model by filling in two major gaps: (1) model resolution is typically too coarse to resolve fine scale processes associated with fires, and (2) chemistry and aerosol processes in fire smoke are poorly represented.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Project report for year 1

In the first year of the project, we developed a fire event tracking system using active fire detections recorded by the Visible Infrared Imaging Radiometer Suite (VIIRS). We employed this algorithm to track all large fires that occurred in California from 2013 to 2020, and created a retrospective Fire Event Data Suite (FEDS) that contains the vector shapes of fire perimeters, active fire fronts, and the locations and fire radiative powers for each fire pixel at 12-hour temporal resolution for each fire event. Using the 2020 Creek Fire as a test bed, we also delineated hourly fire perimeters by combining the FEDS data with the hourly rates of spread derived from Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI) fire data. This dataset will form the basis of the near-real-time hourly fire emissions product that we plan to develop at years 2 and 3 of the project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Global Dust Cycle and Direct Radiative Effect in E3SM Version 1: Impact of Increasing Model Resolution

Quantification of dust aerosols in Earth System Models (ESMs) has important implications for water cycle and biogeochemistry studies. This study examines the global life cycle and direct radiative effects (DREs) of dust in the U.S. Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1), and the impact of increasing model resolution both horizontally and vertically. The default 1° E3SMv1 captures the spatial and temporal variability in the observed dust aerosol optical depth (DAOD) reasonably well, but overpredicts dust absorption in the shortwave (SW). Simulations underestimate the dust vertical and long-range transport, compared with the satellite dust extinction profiles. After updating dust refractive indices and correcting for a bias in partitioning size-segregated emissions, both SW cooling and longwave (LW) warming of dust simulated by E3SMv1 are increased and agree better with other recent studies. The estimated net dust DRE of –0.42 Wm –2 represents a stronger cooling effect than the observationally based estimate –0.2 Wm –2 (–0.48 to +0.2), due to a smaller LW warming. Constrained by a global mean DAOD, model sensitivity studies of increasing horizontal and vertical resolution show strong influences on the simulated global dust burden and lifetime primarily through the change of dust dry deposition rate; there are also remarkable differences in simulated spatial distributions of DAOD, DRE, and deposition fluxes. Thus, constraining the global DAOD is insufficient for accurate representation of dust climate effects, especially in transitioning to higher- or variable-resolution ESMs. Better observational constraints of dust vertical profiles, dry deposition, size, and LW properties are needed.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Effects of organized convection parameterization on the MJO and precipitation in E3SMv1. Part I: Mesoscale heating

Mesoscale organization of convection is typically not represented in global circulation models, and hence its influence on the global circulation is not accounted for. The heating component of a parameterization that represents the dynamical and physical effects of circulations associated with organized convection, referred to as the multiscale coherent structure parameterization (MCSP), is implemented in the Energy Exascale Earth System Model version 1 (E3SMv1). Numerical simulations are conducted to assess its impact on the simulated climate. Besides E3SMv1 simulations, we performed high-resolution (1 km) simulations using the Weather Research and Forecasting (WRF) Model to determine the temperature tendencies induced by mesoscale convective systems embedded in deep convection. We tuned the free parameters of the MCSP based on the WRF simulations. MCSP heating enhances Kevin wave spectra in E3SMv1, improves the representation of the Madden-Julian Oscillation, and impacts on mean precipitation that vary with locations and seasons, including a reduction of biases over the tropical Pacific.

Climate Modeling↗
DOE OSTI2020

Fast neural Poincaré maps for toroidal magnetic fields

Poincaré maps for toroidal magnetic fields are routinely employed to study gross confinement properties in devices built to contain hot plasmas. In most practical applications, evaluating a Poincaré map requires numerical integration of a magnetic field line, a process that can be slow and that cannot be easily accelerated using parallel computations. We propose a novel neural network architecture, the HénonNet, and show that it is capable of accurately learning realistic Poincaré maps from observations of a conventional field-line-following algorithm. After training, such learned Poincaré maps evaluate much faster than the field-line integration method. Moreover, the HénonNet architecture exactly reproduces the primary physics constraint imposed on field-line Poincaré maps: flux preservation. Furthermore, this structure-preserving property is the consequence of each layer in a HénonNet being a symplectic map. We demonstrate empirically that a HénonNet can learn to mock the confinement properties of a large magnetic island by using coiled hyperbolic invariant manifolds to produce a sticky chaotic region at the desired island location. This suggests a novel approach to designing magnetic fields with good confinement properties that may be more flexible than ensuring confinement using KAM tori.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

An Overview of the Hybrid Illinois Device for Research and Applications Material Analysis Test-stand (HIDRA-MAT)

The Hybrid Illinois Device for Research and Applications (HIDRA) at the University of Illinois at Urbana-Champaign is a toroidal plasma device that enables fusion plasma-material interaction testing with both stellarator and tokamak plasmas. HIDRA’s long-pulse steady state stellarator plasmas provide a testbed for plasma facing component (PFC) plasma exposures. The HIDRA Material Analysis Test-stand (HIDRA-MAT) is a material characterization module attached to HIDRA that is being designed and fabricated to include thermal desorption spectroscopy and laser induced breakdown spectroscopy systems for in-vacuo PFC characterization. A specialized rotatable sample holder positions the sample for liquid metal droplet application from a liquid metal droplet injector on HIDRA-MAT. Early experiments look to investigate the effect liquid lithium has on porous tungsten samples’ retention of H, D, and He after plasma exposure. Preliminary results from a dual residual gas analyzer system show the ability to differentiate D2 and He in HIDRA-MAT. Finally, this work aims to advance the understanding of liquid metal PFCs and further the design and development of new fusion PFCs and technologies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

The Summertime Precipitation Bias in E3SM Atmosphere Model Version 1 over the Central United States

This study analyzes the summertime precipitation bias over the Central United States and its relationship to the simulated large-scale environment and the convection scheme in the Energy Exascale Earth System Model Atmosphere Model version 1. This relationship is mainly examined in a set of short-term hindcasts initialized with realistic large-scale conditions for the summer of 2011. Besides the uniform 1° model resolution, we adopt Regionally Refined Meshes to increase the model resolution to 0.25°over the contiguous United States. Additional five-year Atmospheric Model Intercomparison Project simulations are conducted to confirm that the results from the hindcasts are consistent with the climate runs. We find that the summertime dry precipitation bias over the Great Plains and the wet bias over the Rockies cannot be reduced simultaneously by changing resolution or tuning parameters. As for the diurnal cycle, Energy Exascale Earth System Model Atmosphere Model version 1 captures the general diurnal variation of the large-scale moisture transport and the large-scale upward motion over the Central United States. However, the diurnal cycle of precipitation over the Great Plains is out of phase with the diurnal variation of the large-scale environment because the convective precipitation dominates the total precipitation and its diurnal cycle, and it does not directly respond to the local moisture convergence and the large-scale upward motion. These results reemphasize the importance of improving the coupling of the convection to the large-scale environment in reducing the summer precipitation bias over the Central United States in climate models with the resolution of ~0.25°.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Entropy-based closure for probabilistic learning on manifolds

In a recent paper, the authors proposed a general methodology for probabilistic learning on manifolds. The method was used to generate numerical samples that are statistically consistent with an existing dataset construed as a realization from a non-Gaussian random vector. The manifold structure is learned using diffusion manifolds and the statistical sample generation is accomplished using a projected Itˆo stochastic differential equation. This probabilistic learning approach has been extended to polynomial chaos representation of databases on manifolds and to probabilistic nonconvex constrained optimization with a fixed budget of function evaluations. The methodology introduces an isotropic-diffusion kernel with hyperparameter ". Currently, " is more or less arbitrarily chosen. In this paper, we propose a selection criterion for identifying an optimal value of ", based on a maximum entropy argument. The result is a comprehensive, closed, probabilistic model for characterizing data sets with hidden constraints. This entropy argument ensures that out of all possible models, this is the one that is the most uncertain beyond any specified constraints, which is selected. Applications are presented for several databases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

CAUSES: Attribution of Surface Radiation Biases in NWP and Climate Models near the U.S. Southern Great Plains

Abstract Many Numerical Weather Prediction (NWP) and climate models exhibit too warm lower tropospheres near the midlatitude continents. The warm bias has been shown to coincide with important surface radiation biases that likely play a critical role in the inception or the growth of the warm bias. This paper presents an attribution study on the net radiation biases in nine model simulations, performed in the framework of the CAUSES project (Clouds Above the United States and Errors at the Surface). Contributions from deficiencies in the surface properties, clouds, water vapor, and aerosols are quantified, using an array of radiation measurement stations near the Atmospheric Radiation Measurement Southern Great Plains site. Furthermore, an in‐depth analysis is shown to attribute the radiation errors to specific cloud regimes. The net surface shortwave radiation is overestimated in all models throughout most of the simulation period. Cloud errors are shown to contribute most to this overestimation, although nonnegligible contributions from the surface albedo exist in most models. Missing deep cloud events and/or simulating deep clouds with too weak cloud radiative effects dominate in the cloud‐related radiation errors. Some models have compensating errors between excessive occurrence of deep cloud but largely underestimating their radiative effect, while other models miss deep cloud events altogether. Surprisingly, even the latter models tend to produce too much and too frequent afternoon surface precipitation. This suggests that rather than issues with the triggering of deep convection, cloud radiative deficiencies are related to too weak convective cloud detrainment and too large precipitation efficiencies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

CAUSES: On the Role of Surface Energy Budget Errors to the Warm Surface Air Temperature Error Over the Central United States

Many weather forecasting and climate models simulate a warm surface air temperature (T2m) bias over mid-latitude continents during the summertime, especially over the Great Plains. We present here one of a series of papers from a multi-model intercomparison project (CAUSES: Cloud Above the United States and Errors at the Surface), which aims to evaluate the role of cloud, radiation, and precipitation biases in contributing to T2m bias using a short-term hindcast approach with observations mainly from the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site during the period of April to August 2011. The present study examines the contribution of surface energy budget errors to the bias. All participating models simulate higher net shortwave and longwave radiative fluxes at the surface but there is no consistency on signs of biases in latent and sensible heat fluxes over the Central U.S. and ARM SGP. Nevertheless, biases in net shortwave and downward longwave fluxes, as well as surface evaporative fraction (EF) are the main contributors to T2m bias. Radiation biases are largely affected by cloud simulations, while EF is affected by soil moisture modulated by seasonal accumulated precipitation and evaporation. An approximate equation is derived to further quantify the magnitudes of radiation and EF contributions to T2m bias. Our analysis suggests that radiation errors are always an important source of T2m error for long-term climate runs with EF errors either of equal or lesser importance. However, for the short-term hindcasts, EF errors are more important provided a model has a substantial EF bias.

Ma, H. -Y.↗
DOE OSTI2017

Cross-scale intercomparison of climate change impacts simulated by regional and global hydrological models in eleven large river basins

Ideally, the results from models operating at different scales should agree in trend direction and magnitude of impacts under climate change. However, this implies that the sensitivity of impact models designed for either scale to climate variability and change is comparable. In this study, we compare hydrological changes simulated by 9 global and 9 regional hydrological models (HM) for 11 large river basins in all continents under reference and scenario conditions. The foci are on model validation runs, sensitivity of annual discharge to climate variability in the reference period, and sensitivity of the long-term average monthly seasonal dynamics to climate change. One major result is that the global models, mostly not calibrated against observations, often show a considerable bias in mean monthly discharge, whereas regional models show a much better reproduction of reference conditions. However, the sensitivity of two HM ensembles to climate variability is in general similar. The simulated climate change impacts in terms of long-term average monthly dynamics evaluated for HM ensemble medians and spreads show that the medians are to a certain extent comparable in some cases with distinct differences in others, and the spreads related to global models are mostly notably larger. Summarizing, this implies that global HMs are useful tools when looking at large-scale impacts of climate change and variability, but whenever impacts for a specific river basin or region are of interest, e.g. for complex water management applications, the regional-scale models validated against observed discharge should be used.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

2D Gridded Surface Data Value-Added Product

This report describes the Atmospheric Radiation Measurement (ARM) Best Estimate (ARMBE) 2-dimensional (2D) gridded surface data (ARMBE2DGRID) value-added product. Spatial variability is critically important to many scientific studies, especially those that involve processes of great spatial variations at high temporal frequency (e.g., precipitation, clouds, radiation, etc.). High-density ARM sites deployed at the Southern Great Plains (SGP) allow us to observe the spatial patterns of variables of scientific interests. The upcoming megasite at SGP with its enhanced spatial density will facilitate the studies at even finer scales. Currently, however, data are reported only at individual site locations at different time resolutions for different datastreams. It is difficult for users to locate all the data they need and requires extra effort to synchronize the data. To address these problems, the ARMBE2DGRID value-added product merges key surface measurements at the ARM SGP sites and interpolates the data to a regular 2D grid to facilitate the data application.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

Station-based Surface Data Value-Added Product

This report describes the Atmospheric Radiation Measurement (ARM) Best Estimate (ARMBE) station-based surface data (ARMBESTNS) value-added product. It is a twin data product of the ARMBE 2-Dimensional gridded (ARMBE2DGRID) data set. Unlike the ARMBE2DGRID data set, ARMBESTNS data are reported at the original site locations and show the original information (except for the interpolation over time). Therefore, the users have the flexibility to process the data with the approach more suitable for their applications. This document provides information about the input data, quality control (QC) method, and output format of this data set. As much of the information is identical to that of the ARMBE2DGRID data, this document will emphasize more on the different aspects of these two data sets.

97 MATHEMATICS AND COMPUTING↗