Assimilation of POLDER observations to estimate aerosol emissions

We apply a local ensemble transform Kalman smoother (LETKS) in combination with the global aerosol–climate model ECHAM–HAM to estimate aerosol emissions from POLDER-3/PARASOL (POLarization and Directionality of the Earth's Reflectances) observations for the year 2006. We assimilate aerosol optical depth at 550 mnm (AOD550), the Ångström exponent at 550 and 865 nm (AE550–865), and single-scattering albedo at 550 nm (SSA550) in order to improve modeled aerosol mass, size and absorption simultaneously. The new global aerosol emissions increase to 1419 Tg yr−1 (+28 %) for dust, 1850 Tg yr−1 (+75 %) for sea salt, 215 Tg yr−1 (+143 %) for organic aerosol and 13.3 Tg yr−1 (+75 %) for black carbon, while the sulfur dioxide emissions increase to 198 Tg yr−1 (+42 %) and the total deposition of sulfates to 293 Tg yr−1 (+39 %). Organic and black carbon emissions are much higher than their prior values from bottom-up inventories, with a stronger increase in biomass burning sources (+193 % and +90 %) than in anthropogenic sources (115 % and 70 %). The evaluation of the experiments with POLDER (assimilated) and AERONET as well as MODIS Dark Target (independent) observations shows a clear improvement compared with the ECHAM–HAM control run. Specifically based on AERONET, the global mean error in AOD550 improves from −0.094 to −0.006, while absorption aerosol optical depth at 550 nm (AAOD550) improves from −0.009 to −0.004 after the assimilation. A smaller improvement is also observed in the AE550–865 mean absolute error (from 0.428 to 0.393), with a considerably higher improvement over isolated island sites at the ocean. The new dust emissions are closer to the ensemble median of AEROCOM I, AEROCOM III and CMIP5 as well as some of the previous assimilation studies. The new sea salt emissions have become closer to the reported emissions from previous studies. Indications of a missing fraction of coarse dust and sea salt particles are discussed. The biomass burning changes (based on POLDER) can be used as alternative biomass burning scaling factors for the Global Fire Assimilation System (GFAS) inventory distinctively estimated for organic carbon (2.93) and black carbon (1.90) instead of the recommended scaling of 3.4 (Kaiser et al., 2012). The estimated emissions are highly sensitive to the relative humidity due to aerosol water uptake, especially in the case of sulfates. We found that ECHAM–HAM, like most of the global climate models (GCMs) that participated in AEROCOM and CMIP6, overestimated the relative humidity compared with ERA5 and as a result the water uptake by aerosols, assuming the kappa values are not underestimated. If we use the ERA5 relative humidity, sulfate emissions must be further increased, as modeled sulfate AOD is lowered. Specifically, over East Asia, the lower AOD can be attributed to the underestimated precipitation and the lack of simulated nitrates in the model.</p

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Improvement of aerosol optical properties modeling over Eastern Asia with MODIS AOD assimilation in a global non-hydrostatic icosahedral aerosol transport model

A new global aerosol assimilation system adopting a more complex icosahedral grid configuration is developed. Sensitivity tests for the assimilation system are performed utilizing satellite retrieved aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS), and the results over Eastern Asia are analyzed. The assimilated results are validated through independent Aerosol Robotic Network (AERONET) observations. Our results reveal that the ensemble and local patch sizes have little effect on the assimilation performance, whereas the ensemble perturbation method has the largest effect. Assimilation leads to significantly positive effect on the simulated AOD field, improving agreement with all of the 12 AERONET sites over the Eastern Asia based on both the correlation coefficient and the root mean square difference (assimilation efficiency). Meanwhile, better agreement of the Ångström Exponent (AE) field is achieved for 8 of the 12 sites due to the assimilation of AOD only

Assimilation of MODIS Dark Target and Deep Blue Observations in the Dust Aerosol Component of NMMB-MONARCH version 1.0

A data assimilation capability has been built for the NMMB-MONARCH chemical weather prediction system, with a focus on mineral dust, a prominent type of aerosol. An ensemble-based Kalman filter technique (namely the local ensemble transform Kalman filter - LETKF) has been utilized to optimally combine model background and satellite retrievals. Our implementation of the ensemble is based on known uncertainties in the physical parametrizations of the dust emission scheme. Experiments showed that MODIS AOD retrievals using the Dark Target algorithm can help NMMB-MONARCH to better characterize atmospheric dust. This is particularly true for the analysis of the dust outflow in the Sahel region and over the African Atlantic coast. The assimilation of MODIS AOD retrievals based on the Deep Blue algorithm has a further positive impact in the analysis downwind from the strongest dust sources of the Sahara and in the Arabian Peninsula. An analysis-initialized forecast performs better (lower forecast error and higher correlation with observations) than a standard forecast, with the exception of underestimating dust in the long-range Atlantic transport and degradation of the temporal evolution of dust in some regions after day 1. Particularly relevant is the improved forecast over the Sahara throughout the forecast range thanks to the assimilation of Deep Blue retrievals over areas not easily covered by other observational datasets.The present study on mineral dust is a first step towards data assimilation with a complete aerosol prediction system that includes multiple aerosol species

Investigating the assimilation of CALIPSO global aerosol vertical observations using a four-dimensional ensemble Kalman filter

Aerosol vertical information is critical to quantify the influences of aerosol on the climate and environment; however, large uncertainties still persist in model simulations. In this study, the vertical aerosol extinction coefficients from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) are assimilated to optimize the hourly aerosol fields of the Non-hydrostatic ICosahedral Atmospheric Model (NICAM) online coupled with the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) using a four-dimensional local ensemble transform Kalman filter (4-D LETKF). A parallel assimilation experiment using bias-corrected aerosol optical thicknesses (AOTs) from the Moderate Resolution Imaging Spectroradiometer (MODIS) is conducted to investigate the effects of assimilating the observations (and whether to include vertical information) on the model performances. Additionally, an experiment simultaneously assimilating both CALIOP and MODIS observations is conducted. The assimilation experiments are successfully performed for 1 month, making it possible to evaluate the results in a statistical sense. The hourly analyses are validated via both the CALIOP-observed aerosol vertical extinction coefficients and the AOT observations from MODIS and the AErosol RObotic NETwork (AERONET). Our results reveal that both the CALIOP and MODIS assimilations can improve the model simulations. The CALIOP assimilation is superior to the MODIS assimilation in modifying the incorrect aerosol vertical distributions and reproducing the real magnitudes and variations, and the joint CALIOP and MODIS assimilation can further improve the simulated aerosol vertical distribution. However, the MODIS assimilation can better reproduce the AOT distributions than the CALIOP assimilation, and the inclusion of the CALIOP observations has an insignificant impact on the AOT analysis. This is probably due to the nadir-viewing CALIOP having much sparser coverage than MODIS. The assimilation efficiencies of CALIOP decrease with increasing distances of the overpass time, indicating that more aerosol vertical observation platforms are required to fill the sensor-specific observation gaps and hence improve the aerosol vertical data assimilation

Effects of data assimilation on the global aerosol key optical properties simulations

We present the one month results of global aerosol optical properties for April 2006, using the Spectral Radiation Transport Model for Aerosol Species (SPRINTARS) coupled with the Non-hydrostatic ICosahedral Atmospheric Model (NICAM), by assimilating Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) with Local Ensemble Transform Kalman Filter (LETKF). The simulated AOD, Angstrom Exponent (AE) and single scattering albedo (SSA) are validated by independent Aerosol Robotic Network (AERONET) observations over the global sites. The data assimilation has the strongest positive effect on the AOD simulation and slight positive influences on the AE and SSA simulations. For the time-averaged globally spatial distribution, the data assimilation increases the model skill score (S) of AOD, AE, and SSA from 0.55, 0.92, and 0.75 to 0.79, 0.94, and 0.80, respectively. Over the North Africa (NAF) and Middle East region where the aerosol composition is simple.(mainly dust), the simulated AODs are best improved by the data assimilation, indicating the assimilation correctly modifies the wrong dust burdens caused by the uncertainties of the dust emission parameterization. Assimilation also improves the simulation of the temporal variations of the aerosol optical properties over the AERONET sites, with improved S at 60 (62%), 45 (55%) and 11 (50%) of 97, 82 and 22 sites for AOD, AE and SSA. By analyzing AOD and AE at five selected sites with best S improvement, this study further indicates that the assimilation can reproduce short duration events and ratios between fine and coarse aerosols more accurately. (C) 2016 Elsevier B.V. All rights reserved.National Natural Science Funds of China [41475031, 41571130024, 41130104]; Public Meteorology Special Foundation of MOST [GYHY201406023]; State Key Joint Laboratory of Environment Simulation and Pollution Control [15K02ESPCP]; JAXA/EarthCARE; MEXT/VL for Climate System Diagnostics; MOE/Global Environment Research Fund [14426634]; NIES/GOSAT; NIES/CGER; MEXT/RECCA/SALSA; [A-1101]SCI(E)[email protected]

Assimilation of MODIS Dark Target and Deep Blue observations in the dust aerosol component of NMMB-MONARCH version 1.0

A data assimilation capability has been built for the NMMB-MONARCH chemical weather prediction system, with a focus on mineral dust, a prominent type of aerosol. An ensemble-based Kalman filter technique (namely the local ensemble transform Kalman filter – LETKF) has been utilized to optimally combine model background and satellite retrievals. Our implementation of the ensemble is based on known uncertainties in the physical parametrizations of the dust emission scheme. Experiments showed that MODIS AOD retrievals using the Dark Target algorithm can help NMMB-MONARCH to better characterize atmospheric dust. This is particularly true for the analysis of the dust outflow in the Sahel region and over the African Atlantic coast. The assimilation of MODIS AOD retrievals based on the Deep Blue algorithm has a further positive impact in the analysis downwind from the strongest dust sources of the Sahara and in the Arabian Peninsula. An analysis-initialized forecast performs better (lower forecast error and higher correlation with observations) than a standard forecast, with the exception of underestimating dust in the long-range Atlantic transport and degradation of the temporal evolution of dust in some regions after day 1. Particularly relevant is the improved forecast over the Sahara throughout the forecast range thanks to the assimilation of Deep Blue retrievals over areas not easily covered by other observational datasets. The present study on mineral dust is a first step towards data assimilation with a complete aerosol prediction system that includes multiple aerosol species.This work was funded by the SEV-2011- 00067 grant of the Severo Ochoa Program awarded by the Spanish Government, the CGL-2013-46736-R grant of the Spanish Ministry of Economy and Competitiveness, and the ACTRIS Research Infrastructure Project of the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 654169. The authors thank all the Principal Investigators and their staff for establishing and maintaining the AERONET sites, NRL/University of North Dakota for the MODIS AOD L3 product, and the MODIS and OMI mission scientists and associated NASA personnel for the production of the AOD, AAI, and AE data used in this investigation. The authors thankfully acknowledge the computer resources at MareNostrum and the technical support provided by the Barcelona Supercomputing Center (RES-AECT-2015-1-0007). They also thank Francesco Benincasa for his technical support. Carlos Pérez García-Pando acknowledges long-term support from the AXA Research Fund, as well as the support received through the Ramón y Cajal programme (grant RYC-2015-18690) of the Spanish Ministry of Economy and Competitiveness. Comments from two anonymous reviewers are gratefully acknowledged.Peer Reviewe

Hourly Aerosol Assimilation of Himawari-8 AOT Using the Four-Dimensional Local Ensemble Transform Kalman Filter

The next-generation geostationary satellite Himawari-8 has a much higher observation frequency of the aerosol field than polar-orbiting satellites. Aerosol analyses with a geostationary satellite can advance our understanding of the rapid spatiotemporal evolution of aerosols, which is especially critical for studies of air pollution and its mechanisms. We present a one-monthlong hourly aerosol analysis using an aerosol data assimilation based on the local ensemble Kalman filter (LETKF), Himawari-8-retrieved hourly aerosol optical thicknesses (AOTs), and a global model named Non-hydrostatic Icosahedral Atmospheric Model coupled with an aerosol model named Spectral Radiation Transport Model for Aerosol Species (NICAM-SPRINTARS). To assimilate asynchronous observations and avoid frequent switching between the assimilation and ensemble aerosol forecasts, the LETKF is also extended to the four-dimensional LETKF (4D-LETKF). The hourly aerosol analyses are evaluated with both the assimilated Himawari-8 AOTs and independent Moderate Resolution Imaging Spectroradiometer (MODIS)- and AErosol RObotic NETwork (AERONET)-retrieved AOTs. All evaluations show that the assimilations positively affect the model performances and produce simulated AOTs that are closer to the observations. The analyses correctly reduce the significantly positive biases and root-mean-square errors of the control experiment, especially over East China and Australia. Our results also show that hourly aerosol analyses with more frequent Himawari-8 observations are superior to those using the polar satellite MODIS observations. The performances among the LETKF and 4D-LETKF experiments are generally not so different, but the computational load of the 4D-LETKF is much lighter than that of the LETKF