Search NASA⌕ Search

SEARCH · Search NASA

Results for “TROPOMI”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

TROPOMI Methane Validation in the GeoCarb Domain

Objective: GeoCarb validation •Purpose: •Do the four TCCON sites in the GeoCarb domain characterize satellite validation? •Method: •Assess the error estimates globally vs. in the GeoCarb domain •Assess the use of GML/DOE aircraft for CH4 validation •Systematic error estimate (from Kulawik, 2016): •+ regional bias (stdevof average bias versus validation at each station) •+ correlated error (stdev of daily average versus validation) •-co-location error (above quantities for model@satelliteminus model@valid) •-validation error (systematic error of validation estimates, e.g. model extension) Aircraft validation error •CH4 profile extension error estimate: 7 ppb •Extend aircraft observations to the top of the atmosphere with different model configurations •CAMS, scaling to stratosphere, then taking stratosphere •Extend by repeating top aircraft value to the troposphere, then taking CAMS •Extend with GEOS-Chem run (from 2010). •Difference CAMS-Scale vs. CAMS-Tropo: •3.6 +-5.5 ppb •Systematic error: 6.6 ppb •Extending aircraft to tropopause is blatantly wrong, but these two have the same stratosphere. •Difference CAMS-Scale vs. GEOS-Scale: •6.2 +-8.7 ppb •Do not consider bias since model is 10 years off. Systematic error ~8.7 ppb. •Estimate of systematic error from profile extension (possibly underestimated, resulting in possibly higher TROPOMI error estimate) •7 ppb •Would likely vary some by season, site, other conditions •Aircraft measurement error: 1 ppb •1 ppb from measurement error3

TROMPOMI↗
NASA NTRSDate not supplied

Generating Global CH4 NASA GEOS Product by Assimilating TROPOMI

Radiative properties of methane (CH4) significantly contribute to climate change and the recent acceleration of global CH4 growth requires thorough investigation into its causes. Examination of temporal and spatial CH4 variability is crucial for better understanding the shifts that are currently taking place. Integrating different CH4 observations to create a reliable, easily to use global atmospheric CH4 product could contribute to assessment of emissions processes, but remains challenging due to the lack of long satellite data records. Here we present the NASA Goddard Earth Observing System (GEOS) based global CH4 product constrained by atmospheric transport from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) and assimilated CH4 from the TROPOspheric Monitoring Instrument (TROPOMI). This product uses the Constituent Data Assimilation System (CoDAS) component of the GEOS capability, which has been expanded and generalized to assimilate any satellite, ground-based, or in situ observation of atmospheric composition that can be expressed as a vertical sounding or point sample. The product is similar to the one generated by assimilating carbon dioxide (CO2) data into GEOS from Orbiting Carbon Observatory 2 (OCO-2): OCO-2 GEOS Level 3 daily and monthly, 0.5x0.625 assimilated CO2 V10r. The preliminary product is investigated using a variety of quality check approaches with the help of in situ observations to examine bias correction, error inflation, and performance of the assimilation system. Potential applications of this approach include support for interpretation of high-resolution point source detection approaches, climate and greenhouse gas reanalyses, and boundary conditions for regional modeling approaches.

Nikolay Balashov↗
NASA NTRSDate not supplied

PyroCbs from Australia Fires and its Impact Using Satellite Observations from CrIS and TROPOMI and Reanalysis Data

Pyrocumulonimbus (pyroCb) clouds are thunder clouds created by intense heat from the Earth’s surface. They are formed similarly to cumulonimbus clouds, but the intense heat that results in the vigorous updraft comes from fire, either large wildfires or volcanic eruptions. Australia’s unprecedented fire disasters at the end of 2019 to early 2020 emitted huge amounts of carbon monoxide (CO) and fire aerosol particles to the atmosphere, particularly during the pyroCb outbreak that occurred in southeast Australia between 29 December 2019 and 4 January 2020. It was estimated that at least 18 pyroCbs were generated during this episode, and some of them injected ice, smoke, and biomass burning gases above the local tropopause. An unprecedented abundance of H2O and CO in the stratosphere, and the displacement of background ozone (O3) and N2O from rapid ascent of air from the troposphere and lower stratosphere were found from satellite observations. Some other studies also found that the fire emissions and their long-range transport resulted in stratospheric aerosol, temperature, and O3 anomalies after the 2020 Australian bushfires and altered the Antarctic ozone and vortex, posing great impact to local air quality and climate change. Further study on the atmospheric thermodynamic status of atmosphere associated with these pyroCbs, and the change of the cloud properties and trace gases during this unprecedented Australia fires will be made using a new single Field of View (SFOV) Sounder Atmospheric Products (SiFSAP). SiFSAP was developed by NASA using the Cross-track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) onboard SNPP and JPSS-1, and will soon be available to the public at NASA DAAC. Since this product has a spatial resolution of 15 km at nadir, which is better than most global weather and climate models and other current operational sounding products, a process-oriented analysis of the dynamic transport of CO and fire plumes during this unprecedented fire disasters will be made in this study. Based on a Principal Component Radiative Transfer Model (PCRTM) and an optimized estimation retrieval algorithm, a simultaneously retrieval is made using the whole spectral information measured by CrIS, and the derived SiFSAP include temperature, water vapor, trace gases (such as O3, CO2, CO, CH4 and N2O), cloud properties and surface properties. Use of ATMS together with CrIS allows SiFSAP to get accurate retrieval products under thick pyroCb conditions, and an algorithm to detect pyroCb based on the hyperspectral infrared sounder spectrum from CrIS will be developed and verified. In addition to SiFSAP sounding products, other products like CO, O3, NO2 from TROPOMI, O3 from OMPS will be used for retrospective analysis. The wind fields from the NASA’s Modern-Era Retrospective Analysis for Research and Applications Version-2 (MERRA-2) and ERA5 will be used to characterize the transport, and the SiFSAP temperature and water vapor profiles within and around pyroCbs will be compared with MERRA-2 and ERA5 products.

Xiaozhen (Shawn) Xiong↗
NASA NTRSDate not supplied

Generating a 4D Global CH(4) Product by Assimilating TROPOMI column CH(4) in NASA’s GEOS GCM

Examination of temporal and spatial CH4 variability is crucial for better understanding the human and natural processes driving climate change and ultimately designing mitigation strategies. Here we present an analysis framework that uses NASA’s GEOS General Circulation Model (GCM) to construct a high-resolution, time varying picture of atmospheric CH4 consistent with measurements from a variety of platforms, both in situ and remotely sensed. The resulting time varying atmospheric CH4 product can (i) support interpretation of high-resolution point source detection approaches, (ii) provide reanalysis fields for CH4 and other greenhouse gases, and (iii) supply boundary conditions for regional models. Our approach starts with a set of CH4 emissions from various inventories that have been adjusted to match the global annual growth rate over recent decades. These emissions are transported by the GEOS GCM, which in turn is constrained by meteorology from NASA’s Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) product. The simulated atmospheric field is compared with CH4 measurements, such as those from the TROPOspheric Monitoring Instrument (TROPOMI), and adjustments calculated following a Bayesian protocol. The accuracy of the resultant optimal atmospheric CH4 field can be demonstrated by its improved agreement (compared to a direct simulation of the CH4 inventories) with a host of independent CH4 measurements, such as those from the Total Carbon Column Observation Network (TCCON) and in situ observations from surface and airborne platforms.

Nikolay V. Balashov↗
NASA NTRS2024

Revised estimates of NO 2 reductions during the COVID-19 lockdowns using updated TROPOMI NO 2 retrievals and model simulations

The TROPOspheric Monitoring Instrument (TROPOMI) observed unprecedented declines in NO 2 vertical column densities (VCD) over the world's most densely populated cities during the 2020 COVID-19 lockdowns. These favorable changes in NO 2 air quality were correlated with sharp reductions in traffic volume and economic activity during the lockdowns. In this comprehensive global study, we provide revised estimates of the declines in anthropogenic emissions for 36 megacities using a novel methodology for disentangling the anthropogenic emissions from the meteorological transport and natural variability. We further quantify the uncertainty associated with changes in the a priori profile shape information during the lockdowns due to reduced emissions. Satellite NO 2 retrieval techniques calculate an air mass factor that requires a priori NO 2 profile shape information representative of the local atmosphere. This information, which is typically obtained from a chemical transport model (CTM), was not available for the early studies. This study also accounts for the satellite sampling errors resulting from the selective sampling of non-cloudy scenes during the study period. For our analysis, we used CTM simulations that were generated with and without COVID-impacted emissions. We perform retrievals of tropospheric NO 2 columns with the NASA NO 2 algorithm, and then use observed and simulated data to disentangle the meteorological transport from the contribution due anthropogenic emissions. We found that the meteorological transport was most significant source of variability ranging between −35% and 22% of the change total tropospheric VCD. We also find that not accounting for changes in the a priori NO 2 profile shape information during the lockdowns resulted in systematic retrieval errors that were up to 12% of the estimated decline, and the elimination of cloud contaminated scenes resulted in sampling errors that in general ranged between varied ±15%.

NO2↗
NASA NTRS2019

A TROPOMI- and GLM-Based Estimate of NOx Production by Lightning over the U.S.

Lightning produces NO because the extreme temperatures (>20000 K) in lightning channels dissociate molecular O2 and molecular N2, which then combine to form NOx which quickly reacts with O3 to form NO2. Lightning is responsible for 10-15% of NOx emissions globally. This is 2 – 8 Tg N a-1 [Schumann and Huntrieser, 2007] or 100 to 400 mol per flash. Much of the uncertainty stems from limited knowledge of lightning NOx production per flash (LNOx PE) or per unit flash length. Most LNOx is injected into mid- and upper-troposphere where away from deep convection its lifetime is longer relative to lower troposphere NOx. NOx in this region enhances the concentrations of upper tropospheric NOy, OH, and O3 and contributes to positive radiative forcing by O3 and negative forcing by CH4. We have previously used OMI NO2 to obtain estimates of LNOx production per flash over the Gulf of Mexico (Pickering et al., 2016, JGR), in convective events during NASA’s TC4 field program (Bucsela et al., 2010, JGR), and over broad regions of the tropics (Allen et al., 2019, JGR) and midlatitudes (Bucsela et al., 2019, JGR). In the latter studies, we obtained PE values of 170 ± 100 mol flash and 180 ± 100 mol flash, respectively.

Allen, Dale↗