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NASA NTRS2011

Evapotranspiration as a Regional Climate Priority: Results from a NASA/USDA Workshop

On April 5 to 7, 2011, the National Aeronautics and Space Administration (NASA) and the United States Department of Agriculture-Agricultural Research Service (USDA-ARS) sponsored a Workshop on Evapotranspiration (ET) in Silver Spring Maryland. The workshop was a response to a recommendation in the 2009-2011 GEO (Group on Earth Observations) Work Plan that a workshop on ET should be held to discuss issues related to ET products and services and the potential for incorporating ET activities into the 2012-2015 GEO Work Plan. The workshop had a regional emphasis, although there were several excellent international and global presentations including one on the GEWEX LANDFLUX project. The different scales of these activities suggests that a framework is needed that can accommodate both regional and global ET activities. Despite limitations with the workshop's scheduling, it attracted 76 experts who contributed informative presentations and insightful discussions. The goals of the workshop involved the exchange of information and ideas and the development of plans for providing more visibility for ET issues. Specific objectives included 1) defining the needs and requirements for evapotranspiration data in weather and climate studies, in natural and agro-ecoystem monitoring, and in water resource management; 2) reviewing the methods used to measure and model evapotranspiration; 3) assessing surface and satellite observation systems required to support ET measurement, modeling and evaluation; 4) assessing the feasibility of developing a proposal for a task on evapotranspiration for the 2012-2015 GEO Work Plan, and 5) exploring the level of support and consensus for developing a strategy for establishing evapotranspiration as an Essential Climate Variable (ECV) within the Global Climate Observing System (GCOS) framework The workshop featured a combination of oral presentations and breakout group sessions focused on the above objectives. There were also poster presentations providing opportunities for one-on-one discussions of ET modeling and measurement techniques. Presentations by users of ET data set the tone for the workshop. In the USA at the national and regional levels water rights issues represent a major opportunity for ET applications. ET data play a major role in estimating water loss due to irrigation, the largest cause of consumptive water loss in the USA, particularly in the West. Irrigation requirements are relatively specific since the needs are clearly defined by the geometry and number of the irrigation systems and can be monitored with high resolution satellite data. There was a strong consensus that land surface temperature (LST) at high resolution is critical for monitoring irrigation. State governments have made commitments to more efficient water management in the western US, but they need full access to improved and more timely ET data and applications to implement this plan. Water managers also reported that in spite of the recent development of. new techniques, the procedures used in some of the water balance calculations in some states are out of date and do not take advantage of new observational and data assimilation systems. The development of ET forecasts for water management is also seen as a priority. Although ET forecasts are currently being produced on an experimental basis these predictions could be improved by considering ET as a dynamic prediction variable in models and by increasing the time resolution of these ET predictions.

Lawford, Richard↗
NASA NTRS2011

First Assessments of ICESat-2 Performance Using Aircraft Data

The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) Is a next-generation laser altimeter designed to continue key observations of ice sheet elevation change, sea ice freeboard, vegetation canopy height, earth surface elevation, and sea surface height. Scheduled for launch in early 2016, ICESat-2 will use a high repetition rate (approximately 10 kHz), small footprint (10m diameter) laser, and a single-photon-sensitive detection strategy (photon counting) to measure precise ranges to the earth's surface. Operating in the green (approximately 532nm), the six beams of ICESat-2 will provide improved spatial coverage compared with ICESat while the differences in transmit energy among the beams provide a large dynamic range. In order to evaluate models of predicted ICESat-2 performance, and provide ICESat-2-like data for algorithm development an airborne ICESat-2 simulator was developed and first flown in 2010, this simulator, the Multiple Altimeter Beam Experimental Lidar (MABEL) has now had three deployments in the western US, and will be deployed to the polar regions in spring of 2012. MABEL uses a similar measurement strategy to what will be used on ICESat-2. MABEL collects more spatially-dense data than ICESat-2 (approximately 2-cm along track) with a smaller 2m diameter footprint in 16 green channels and an additional 8 channels in the infrared. The comparison between frequencies allows for analysis of possible penetration of green energy into water or snow. We present MABEL data collected over deserts, forests, ocean water, lakes. snow covered mountains, and saft flats, provide examples of how these data are being used to develop algorithms that derive geophysical products from ICESat 2 and assess expected performances.

Neumann, Tom↗
NASA NTRS2012

Drought Monitoring for 3 North American Case Studies Based on the North American Land Data Assimilation System (NLDAS)

Both NLDAS Phase 1 (1996-2007) and Phase 2 (1979-present) datasets have been evaluated against in situ observational datasets, and NLDAS forcings and outputs are used by a wide variety of users. Drought indices and drought monitoring from NLDAS were recently examined by Mo et al. (2010) and Sheffield et al. (2010). In this poster, we will present results analyzing NLDAS Phase 2 forcings and outputs for 3 North American Case studies being analyzed as part of the NOAA MAPP Drought Task Force: (1) Western US drought (1998- 2004); (2) plains/southeast US drought (2006-2007); and (3) Current Texas-Mexico drought (2011-). We will examine percentiles of soil moisture consistent with the NLDAS drought monitor.

Peters-Lidard, Christa D.↗
NASA NTRS2012

An Overview of the NOAA Drought Task Force

The charge of the NOAA Drought Task Force is to coordinate and facilitate the various MAPP-funded research efforts with the overall goal of achieving significant advances in understanding and in the ability to monitor and predict drought over North America. In order to achieve this, the task force has developed a Drought Test-bed that individual research groups can use to test/evaluate methods and ideas. Central to this is a focus on three high profile North American droughts (1998-2004 western US drought, 2006-2007 SE US drought, 2011- current Tex-Mex drought) to facilitate collaboration among projects, including the development of metrics to assess the quality of monitoring and prediction products, and the development of an experimental drought monitoring and prediction system that incorporates and assesses recent advances. This talk will review the progress and plans of the task force, including efforts to help advance official national drought products, and the development of early warning systems by the National Integrated Drought Information System (NIDIS). Coordination with other relevant national and international efforts such as the emerging NMME capabilities and the international effort to develop a Global Drought Information System (GDIS) will be discussed.

Schubert, S.↗
NASA NTRS2013

Data Assimilation of AIRS Water Vapor Profiles: Impact on Precipitation Forecasts for Atmospheric River Cases Affecting the Western of the United States

Atmospheric rivers are transient, narrow regions in the atmosphere responsible for the transport of large amounts of water vapor. These phenomena can have a large impact on precipitation. In particular, they can be responsible for intense rain events on the western coast of North America during the winter season. This paper focuses on attempts to improve forecasts of heavy precipitation events in the Western US due to atmospheric rivers. Profiles of water vapor derived from from Atmospheric Infrared Sounder (AIRS) observations are combined with GFS forecasts by a three-dimensional variational data assimilation in the Gridpoint Statistical Interpolation (GSI). Weather Research and Forecasting (WRF) forecasts initialized from the combined field are compared to forecasts initialized from the GFS forecast only for 3 test cases in the winter of 2011. Results will be presented showing the impact of the AIRS profile data on water vapor and temperature fields, and on the resultant precipitation forecasts.

Blankenship, Clay↗
NASA NTRS2013

Multitemporal Snow Cover Mapping in Mountainous Terrain for Landsat Climate Data Record Development

A multitemporal method to map snow cover in mountainous terrain is proposed to guide Landsat climate data record (CDR) development. The Landsat image archive including MSS, TM, and ETM+ imagery was used to construct a prototype Landsat snow cover CDR for the interior northwestern United States. Landsat snow cover CDRs are designed to capture snow-covered area (SCA) variability at discrete bi-monthly intervals that correspond to ground-based snow telemetry (SNOTEL) snow-water-equivalent (SWE) measurements. The June 1 bi-monthly interval was selected for initial CDR development, and was based on peak snowmelt timing for this mountainous region. Fifty-four Landsat images from 1975 to 2011 were preprocessed that included image registration, top-of-the-atmosphere (TOA) reflectance conversion, cloud and shadow masking, and topographic normalization. Snow covered pixels were retrieved using the normalized difference snow index (NDSI) and unsupervised classification, and pixels having greater (less) than 50% snow cover were classified presence (absence). A normalized SCA equation was derived to independently estimate SCA given missing image coverage and cloud-shadow contamination. Relative frequency maps of missing pixels were assembled to assess whether systematic biases were embedded within this Landsat CDR. Our results suggest that it is possible to confidently estimate historical bi-monthly SCA from partially cloudy Landsat images. This multitemporal method is intended to guide Landsat CDR development for freshwaterscarce regions of the western US to monitor climate-driven changes in mountain snowpack extent.

Landsat↗
NASA NTRS2014

The East Atlantic - West Russia Teleconnection in the North Atlantic: Climate Impact and Relation to Rossby Wave Propagation

Large-scale winter teleconnection of the East Atlantic - West Russia (EA-WR) over the Atlantic and surrounding regions is examined in order to quantify its impacts on temperature and precipitation and identify the physical mechanisms responsible for its existence. A rotated empirical orthogonal function (REOF) analysis of the upper-tropospheric monthly height field captures successfully the EA-WR pattern and its interannual variation, with the North Atlantic Oscillation as the first mode. EA-WRs climate impact extends from eastern North America to Eurasia. The positive (negative) EA-WR produces positive (negative) temperature anomalies over the eastern US, western Europe and Russia east of Caspian Sea, with negative (positive) anomalies over eastern Canada, eastern Europe including Ural Mountains and the Middle East. These anomalies are largely explained by lower-tropospheric temperature advections. Positive (negative) precipitation anomalies are found over the mid-latitude Atlantic and central Russia around 60E, where lower-level cyclonic (anticyclonic) circulation anomaly is dominant. The eastern Canada and the western Europe are characterized by negative (positive) precipitation anomalies.The EA-WR is found to be closely associated with Rossby wave propagation. Wave activity fluxes show that it is strongly tied to large-scale stationary waves. Furthermore, a stationary wave model (SWM) forced with vorticity transients in the mid-latitude Atlantic (approximately 40N) or diabatic heat source over the subtropical Atlantic near the Caribbean Sea produces well-organized EA-WR-like wave patterns, respectively. Sensitivity tests with the SWM indicate improvement in the simulation of the EA-WR when the mean state is modified to have a positive NAO component that enhances upper-level westerlies between 40-60N.

EA/WR↗
NASA NTRS2015

Enabling Earth Science Measurements with NASA UAS Capabilites

NASA's Airborne Science Program (ASP) maintains a fleet of manned and unmanned aircraft for Earth Science measurements and observations. The unmanned aircraft systems (UAS) range in size from very large (Global Hawks) to medium (SIERRA, Viking) and relatively small (DragonEye). UAS fly from very low (boundary layer) to very high altitude (stratosphere). NASA also supports science and applied science projects using UAS operated by outside companies or agencies. The aircraft and accompanying data and support systems have been used in numerous investigations. For example, Global Hawks have been used to study both hurricanes and atmospheric composition. SIERRA has been used to study ice, earthquake faults, and coral reefs. DragonEye is being used to measure volcanic emissions. As a foundation for NASA's UAS work, Altair and Ikkana not only flew wildfires in the Western US, but also provided major programs for the development of real-time data download and processing capabilities. In early 2014, an advanced L-band Synthetic Aperture Radar (SAR) also flew for the first time on Global Hawk, proving the utility of UAVSAR, which has been flying successfully on a manned aircraft. In this paper, we focus on two topics: 1) the results of a NASA program called UAS-Enabled Earth Science, in which three different science teams flew (at least) two different UAS to demonstrate platform performance, airspace integration, sensor performance, and applied science results from the data collected; 2) recent accomplishments with the high altitude, long-duration Global Hawks, especially measurements from several payload suites consisting of multiple instruments. The latest upgrades to data processing, communications, tracking and flight planning systems will also be described.

Earth Science↗
NASA NTRS2015

Pulling Results Out of Thin Air: Four Years of Ozone and Greenhouse Gas Measurements by the Alpha Jet Atmospheric Experiment (AJAX)

The Alpha Jet Atmospheric eXperiment (AJAX) has been measuring atmospheric ozone, carbon dioxide, methane and meteorological parameters from near the surface to 8000 m since January 2011. The main goals are to study photochemical ozone production and the impacts of extreme events on western US air quality, provide data to support satellite observations and aid in the quantification of emission sources e.g. wildfires, urban outflow, diary and oil and gas. The aircraft is based at Moffett Field and flies multiple times a month to sample vertical profiles at selected sites in California and Nevada, providing long-term data records at these sites. AJAX is also uniquely positioned to launch with short notice sampling flights in rapid response to extreme events e.g. the 2013 Yosemite Rim fire. This talk will focus on the impacts of vertical transport on surface air quality, and investigation of emission sources from diaries and wildfires.

AJAX↗
NASA NTRS2015

Techniques for Estimating Emissions Factors from Forest Burning: ARCTAS and SEAC4RS Airborne Measurements Indicate Which Fires Produce Ozone

Previous studies of emission factors from biomass burning are prone to large errors since they ignore the interplay of mixing and varying pre-fire background CO2 levels. Such complications severely affected our studies of 446 forest fire plume samples measured in the Western US by the science teams of NASA's SEAC4RS and ARCTAS airborne missions. Consequently we propose a Mixed Effects Regression Emission Technique (MERET) to check techniques like the Normalized Emission Ratio Method (NERM), where use of sequential observations cannot disentangle emissions and mixing. We also evaluate a simpler "consensus" technique. All techniques relate emissions to fuel burned using C(sub burn) = delta C(sub tot) added to the fire plume, where C(sub tot) approximately equals (CO2 + CO). Mixed-effects regression can estimate pre-fire background values of Ctot (indexed by observation j) simultaneously with emissions factors indexed by individual species i, delta epsilon lambda tau alpha−x(sub i)/(C(sub burn))i,j., MERET and "consensus" require more than two emissions indicators. Our studies excluded samples where exogenous CO or CH4 might have been fed into a fire plume, mimicking emission. We sought to let the data on 13 gases and particulate properties suggest clusters of variables and plume types, using non-negative matrix factorization (NMF). While samples were mixtures, the NMF unmixing suggested purer burn types. Particulate properties (bscat, babs, SSA, AAE) and gas-phase emissions were interrelated. Finally, we sought a simple categorization useful for modeling ozone production in plumes. Two kinds of fires produced high ozone: those with large fuel nitrogen as evidenced by remnant CH3CN in the plumes, and also those from very intense large burns. Fire types with optimal ratios of delta-NOy/delta- HCHO associate with the highest additional ozone per unit Cburn, Perhaps these plumes exhibit limited NOx binding to reactive organics. Perhaps these plumes exhibit limited NOx binding to reactive organics.

fires↗
NASA NTRS2016

Techniques for Estimating Emissions Factors from Forest Burning: ARCTAS and SEAC4RS Airborne Measurements Indicate which Fires Produce Ozone

Previous studies of emission factors from biomass burning are prone to large errors since they ignore the interplay of mixing and varying pre-fire background CO2 levels. Such complications severely affected our studies of 446 forest fire plume samples measured in the Western US by the science teams of NASA's SEAC4RS and ARCTAS airborne missions. Consequently we propose a Mixed Effects Regression Emission Technique (MERET) to check techniques like the Normalized Emission Ratio Method (NERM), where use of sequential observations cannot disentangle emissions and mixing. We also evaluate a simpler "consensus" technique. All techniques relate emissions to fuel burned using C(burn) = delta C(tot) added to the fire plume, where C(tot) approximately equals (CO2 = CO). Mixed-effects regression can estimate pre-fire background values of C(tot) (indexed by observation j) simultaneously with emissions factors indexed by individual species i, delta, epsilon lambda tau alpha-x(sub I)/C(sub burn))I,j. MERET and "consensus" require more than emissions indicators. Our studies excluded samples where exogenous CO or CH4 might have been fed into a fire plume, mimicking emission. We sought to let the data on 13 gases and particulate properties suggest clusters of variables and plume types, using non-negative matrix factorization (NMF). While samples were mixtures, the NMF unmixing suggested purer burn types. Particulate properties (b scant, b abs, SSA, AAE) and gas-phase emissions were interrelated. Finally, we sought a simple categorization useful for modeling ozone production in plumes. Two kinds of fires produced high ozone: those with large fuel nitrogen as evidenced by remnant CH3CN in the plumes, and also those from very intense large burns. Fire types with optimal ratios of delta-NOy/delta- HCHO associate with the highest additional ozone per unit Cburn, Perhaps these plumes exhibit limited NOx binding to reactive organics. Perhaps these plumes exhibit limited NOx binding to reactive organics

ozone↗
NASA NTRS2017

Ikhana UAS Overview

Ikhana demonstrates capabilities of UAS to overfly and collect sensor data on widespread fires throughout Western US and also demonstrate long-endurance mission capabilities (20-hours+). Ikhana images multiple fires (greater than 4 fires per mission), to showcase extendable mission configuration and ability to either linger over key fires or station over disparate regional fires. Ikhana also demonstrates new UAV-compatible, autonomous sensor for improved thermal characterization of fires. Also it provides automated, on-board, terrain and geo-rectified sensor imagery over the horizon SATCOM links to national fire personnel and Incident commanders.

UAS in the NAS↗
NASA NTRS2017

Testing Extensions of Our Quantitative Daily of San Joaquin Wintertime Aerosols Using MAIAC and Meteorology Without Transport/Transformation Assumptions

The Western US and many regions globally present daunting difficulties in understanding and mapping PM2.5 episodes. We evaluate extensions of a method independent of source-description and transport/transformation. These regions suffer frequent few-day episodes due to shallow mixing; low satellite AOT and bright surfaces complicate the description. Nevertheless, we expect residual errors in our maps of less than 8 ug/m^3 in episodes reaching 60-100 ug/m^3; maps which detail pollution from Interstate 5. Our current success is due to use of physically meaningful functions of MODIS-MAIAC-derived AOD, afternoon mixed-layer height, and relative humidity for a basin in which the latter are correlated. A mixed-effects model then describes a daily AOT-to-PM2.5 relationship. (Note: in other published mixed-effects models, AOT contributes minimally. We seek to extend on these to develop useful estimation methods for similar situations. We evaluate existing but more spotty information on size distribution (AERONET, MISR, MAIA, CALIPSO, other remote sensing). We also describe the usefulness of an equivalent mixing depth for water vapor vs meteorological boundary layer height. Each has virtues and limitations. Finally, we begin to evaluate methods for removing the complications due to detached but polluted layers (which don't mix to the surface) using geographical, meteorological, and remotely sensed data.

Chatfield, Robert B.↗
NASA NTRS2018

Quantitative Daily Maps of PM 2.5 Episodes for California and Other Regions: Satellite Column Water and Optical Depth as Allied Tracers of Dilution

The Western US and many regions globally present daunting difficulties in understanding PM 2.5 episodes. We evaluate extensions of a method independent of modeled source-description and transport/transformation and using several satellite remote sensing products from imaging spectrometers. The San Joaquin Valley (SJV) especially suffers few-day episodes due to shallow mixing; PM 2.5 retrieval suffers low satellite AOT (Aerosol Optical Thickness) and bright surfaces.Nevertheless, we find residual errors in our maps of of typically 5-8 micrograms per cubic meter. Episodes in the Valley reaching 60-100 micrograms per cubic meter. These maps detail pollution from Interstate 5 at the scale of a few kilometers. The maps are based on NASA's MODerate resolution Imaging Spectrometer (MODIS) data at circa 1 kilometer as processed with the Multi-Angle Implementation of Atmospheric Correction. The Bay Area Air Quality Management District has requested that we test our methods in their challenging environment characterized by multiple sub-basins defined by complex topography. Our tests suggest that nearly similar precision may be expected for wintertime conditions with high PM 2.5 . We note difficulties when measured PM 2.5 is less than 8-10 micrograms per cubic meter, but good relative precision when PM 2.5 rises above 20; i.e. in episodes of concern for morbidity and mortality. Our method stresses physically meaningful functions of MODIS-MAIAC (Multi-Angle Implementation of Atmospheric Correction)-derived AOD (Aerosol Optical Depth) and total water vapor column. A mixed-effects statistical model exploiting existing station data works powerfully to allow us daily AOT-to-PM 2.5 relationships that allow a calibration of the map. In those cases where water vapor and particles have generally similar surface sources, using the ratio of AOT / Column_water can improve the daily calibrations so as to reach our quoted precision. We briefly present some cartoon idealizations that explain this success and also the likely reasons that our mixed effects model (or "daily calibration") works; also when it should not work. The combined satellite/mixed-effects model works best for wintertime San Joaquin Valley episodes, where the meteorology of particle and H2O(v) dilution is quite appropriate. We extended and tested the methodology (a) for the Bay Area wintertime situations and (b) for smoke plume events (e.g. the October 2017 fire events of the Sonoma area). Our SJV work was evaluated using NASA's DISCOVER-AQ (Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality) airborne measurements, and by season- long measurements in Fresno. If the composition and size distribution of the aerosols can be assessed for the regions we describe, retrievals should have improved accuracy.

Chatfield, Robert B.↗
NASA NTRS2017

Emissions Relationships Among Western Forest Fire Plumes: I. Emission Factors Free from Mixing Errors

Previous studies of emission factors from biomass burning are prone to largeerrors since they ignore the interplay of mixing and varying pre-fire backgroundCO2 levels. Such complications severely affected our studies of 446 forest fireplume samples measured in the Western US by the science teams of NASAsSEAC4RS and ARCTAS airborne missions. Consequently we propose a MixedEffects Regression Emission Technique (MERET) to check techniques like theNormalized Emission Ratio Method (NERM), where use of sequentialobservations cannot disentangle emissions and mixing. We also evaluate asimpler consensus technique. All techniques relate emissions to fuel burnedusing C burn = Δ Ctot added to the fire plume, where ≈ Ctot (CO2 + CO). Mixed-effectsregression can estimate pre-fire background values of Ctot (indexed byobservation j) simultaneously with emissions factors indexed by individual species i, δέλτα-xi (Cburn )i,j., MERET and consensus require more than twoemissions indicators. Our studies excluded samples where exogenous CO orCH4 might have been fed into a fire plume, mimicking emission.We sought to let the data on 13 gases and particulate properties suggest clustersof variables and plume types, using non-negative matrix factorization (NMF).While samples were mixtures, the NMF unmixing suggested purer burn types.Particulate properties (bscat, babs, SSA, AÅE) and gas-phase emissions were interrelated.Finally, we sought a simple categorization useful for modeling ozone productionin plumes. Two kinds of fires produced high ozone: those with large fuel nitrogenas evidenced by remnant CH3CN in the plumes, and also those from veryintense large burns. Fire types with optimal ratios of delta-NOydelta-HCHO associate with the highest additional ozone per unit Cburn, Perhaps theseplumes exhibit limited NOx binding to reactive organics. Perhaps these plumesexhibit limited NOx binding to reactive organics.

plumes↗
NASA NTRS2016

Emissions Relationships Among Western Forest Fire Plumes: II. Plume Typing for Sources of Ozone and Aerosol Absorption

Previous studies of emission factors from biomass burning are prone to largeerrors since they ignore the interplay of mixing and varying pre-fire backgroundCO2 levels. Such complications severely affected our studies of 446 forest fireplume samples measured in the Western US by the science teams of NASAs SEAC4RS and ARCTAS airborne missions. Consequently we propose a MixedEffects Regression Emission Technique (MERET) to check techniques like theNormalized Emission Ratio Method (NERM), where use of sequentialobservations cannot disentangle emissions and mixing. We also evaluate asimpler consensus technique. All techniques relate emissions to fuel burnedusing Cburn Ctot added to the fire plume, where Ctot (CO2 + CO). Mixed-effectsregression can estimate pre-fire background values of Ctot (indexed byobservation j) simultaneously with emissions factors indexed by individualspecies i, xi (Cburn )i,j., MERET and consensus require more than twoemissions indicators. Our studies excluded samples where exogenous CO orCH4 might have been fed into a fire plume, mimicking emission.We sought to let the data on 13 gases and particulate properties suggest clustersof variables and plume types, using non-negative matrix factorization (NMF).While samples were mixtures, the NMF unmixing suggested purer burn types.Particulate properties (bscat, babs, SSA, AE) and gas-phase emissions were interrelated.Finally, we sought a simple categorization useful for modeling ozone productionin plumes. Two kinds of fires produced high ozone: those with large fuel nitrogenas evidenced by remnant CH3CN in the plumes, and also those from veryintense large burns. Fire types with optimal ratios of delta-NOydelta-HCHO associate with the highest additional ozone per unit Cburn, Perhaps theseplumes exhibit limited NOx binding to reactive organics. Perhaps these plumesexhibit limited NOx binding to reactive organics.

ozone↗
NASA NTRS2019

Global Disease Outbreaks Associated with the 2015-2016 El Niño Event

Interannual climate variability patterns associated with the El Niño-Southern Oscillation phenomenon result in climate and environmental anomaly conditions in specific regions worldwide that directly favor outbreaks and/or amplification of variety of diseases of public health concern including chikungunya, hantavirus, Rift Valley fever, cholera, plague, and Zika. We analyzed patterns of some disease outbreaks during the strong 2015–2016 El Niño event in relation to climate anomalies derived from satellite measurements. Disease outbreaks in multiple El Niño-connected regions worldwide (including Southeast Asia, Tanzania, western US, and Brazil) followed shifts in rainfall, temperature, and vegetation in which both drought and flooding occurred in excess (14–81% precipitation departures from normal). These shifts favored ecological conditions appropriate for pathogens and their vectors to emerge and propagate clusters of diseases activity in these regions. Our analysis indicates that intensity of disease activity in some ENSO-teleconnected regions were approximately 2.5–28% higher during years with El Niño events than those without. Plague in Colorado and New Mexico as well as cholera in Tanzania were significantly associated with above normal rainfall (p < 0.05); while dengue in Brazil and southeast Asia were significantly associated with above normal land surface temperature (p < 0.05). Routine and ongoing global satellite monitoring of key climate variable anomalies calibrated to specific regions could identify regions at risk for emergence and propagation of disease vectors. Such information can provide sufficient lead-time for outbreak prevention and potentially reduce the burden and spread of ecologically coupled diseases.

El Nino↗
NASA NTRS2019

Emissions Relationships in Western Forest Fire Plumes: I. Reducing the Effect of Mixing Errors on Emission Factors

Studies of emission factors from biomass burning using aircraft data complement the results of lab studies and extend them to conditions of immense hot conflagrations. We illustrate and discuss emission relationships for 422 individual samples from many forest-fire plumes in the Western US. The samples are from two NASA investigations: ARCTAS (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites) and SEAC4RS (Studies of Emissions and Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys). This work provides sample-by-sample enhancement ratios (EnRs) for 23 gases and particulate properties. Many EnRs provide candidates for emission ratios (ERs, corresponding to the EnR at the source) when the origin and degree of transformation is understood and appropriate. From these, emission factors (EFs) can be estimated when the fuel dry mass consumed is known or can be estimated using the carbon mass budget approach. This analysis requires understanding the interplay of mixing of the plume with surrounding air. Some initial examples emphasize that measured C(tot) = CO2 + CO in a fire plume does not necessarily describe the emissions of the total carbon liberated in the flames, C(burn). Rather, it represents C(tot) = C(burn) + C(bkgd), which includes possibly varying background concentrations for entrained air. Consequently, we present a simple theoretical description for plume entrainment for multiple tracers from flame to hundreds of kilometers downwind and illustrate some intrinsic linear behaviors. The analysis suggests a Mixed Effects Regression Emission Technique (MERET), which can eliminate occasional strong biases associated with the commonly used normalized excess mixing ratio (NEMR) method. MERET splits C(tot) to reveal C(burn) by exploiting the fact that C(burn) and all tracers respond linearly to dilution, while each tracer has consistent EnR behavior (slope of tracer concentration with respect to C(burn)). The two effects are separable. Two or three or preferably more emission indicators are required as a minimum; here we used ten. Limited variations in the EnRs for each tracer can be incorporated and the variations and co-variations analyzed. The percentage CO yield (or the modified combustion efficiency) plays some role. Other co-relationships involving nitrogen and organic classes are more prominent; these have strong relationships to the C(burn) to O3 emission relationship. In summary, MERET allows fine spatial resolution (EnRs for individual observations) and comparison of similar plumes distant in time and space. Alkene ratios provide us with an approximate photochemical timescale. This allows discrimination and definition, by fire situation, of ERs, allowing us to estimate emission factors.

Chatfield, Robert B.↗