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NASA NTRS2018

Assessing the Skill of Chlorophyll Forecasts: Latest Development and Challenges Ahead Using the Case of the Equatorial Pacific

Using a global ocean biogeochemical model combined with a forecast of physical oceanic and atmospheric variables from the NASA Global Modeling and Assimilation Office, we assess the skill of a chlorophyll concentrations forecast in the Equatorial Pacific for the period 2012-2015 with a focus on the forecast of the onset of the 2015 El Nino event. Using a series of retrospective 9-month hindcasts, we assess the uncertainties of the forecasted chlorophyll by comparing the monthly total chlorophyll concentration from the forecast with the corresponding monthly ocean chlorophyll data from the Suomi-National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (S-NPP VIIRS) satellite. The forecast was able to reproduce the phasing of the variability in chlorophyll concentration in the Equatorial Pacific, including the beginning of the 2015-2016 El Nino. The anomaly correlation coefficient (ACC) was significant (p less than 0.05) for forecast at 1-month (R=0.33), 8-month (R=0.42) and 9-month (R=0.41) lead times. The root mean square error (RMSE) increased from 0.0399 microgram chl L(exp -1) for the 1-month lead forecast to a maximum of 0.0472 microgram chl L(exp -1) for the 9-month lead forecast indicating that the forecast of the amplitude of chlorophyll concentration variability was getting worse. Forecasts with a 3-month lead time were on average the closest to the S-NPP VIIRS data (23% or 0.033 microgram chl L(exp -1)) while the forecast with a 9-month lead time were the furthest (31% or 0.042 microgram chl L(exp -1)). These results indicate the potential for forecasting chlorophyll concentration in this region but also highlights various deficiencies and suggestions for improvements to the current biogeochemical forecasting system. This system provides an initial basis for future applications including the effects of El Nino events on fisheries and other ocean resources given improvements identified in the analysis of these results.

El Nin↗
NASA NTRS2018

Improved Hydrology over Peatlands in a Global Land Modeling System

Peatlands of the Northern Hemisphere represent an important carbon pool that mainly accumulated since the last ice age under permanently wet conditions in specific geological and climatic settings. The carbon balance of peatlands is closely coupled to water table dynamics. Consequently, the future carbon balance over peatlands is strongly dependent on how hydrology in peatlands will react to changing boundary conditions, e.g. due to climate change or regional water level drawdown of connected aquifers or streams. Global land surface modeling over organic-rich regions can provide valuable global-scale insights on where and how peatlands are in transition due to changing boundary conditions. However, the current global land surface models are not able to reproduce typical hydrological dynamics in peatlands well. We implemented specific structural and parametric changes to account for key hydrological characteristics of peatlands into NASA's GEOS-5 Catchment Land Surface Model (CLSM, Koster et al. 2000). The main modifications pertain to the modeling of partial inundation, and the definition of peatland-specific runoff and evapotranspiration schemes. We ran a set of simulations on a high performance cluster using different CLSM configurations and validated the results with a newly compiled global in-situ dataset of water table depths in peatlands. The results demonstrate that an update of soil hydraulic properties for peat soils alone does not improve the performance of CLSM over peatlands. However, structural model changes for peatlands are able to improve the skill metrics for water table depth. The validation results for the water table depth indicate a reduction of the bias from 2.5 to 0.2 m, and an improvement of the temporal correlation coefficient from 0.5 to 0.65, and from 0.4 to 0.55 for the anomalies. Our validation data set includes both bogs (rain-fed) and fens (ground and/or surface water influence) and reveals that the metrics improved less for fens. In addition, a comparison of evapotranspiration and soil moisture estimates over peatlands will be presented, albeit only with limited ground-based validation data. We will discuss strengths and weaknesses of the new model by focusing on time series of specific validation sites.

Bechtold, M.↗
NASA NTRS2017

Continuation of Long-Term Global SO2 Pollution Monitoring from OMI to OMPS

Over the past 20 years, advances in satellite remote sensing of pollution-relevant species have made space-borne observations an increasingly important part of atmospheric chemistry research and air quality management. This progress has been facilitated by advanced UV-vis spectrometers, such as the Ozone Monitoring Instrument (OMI) on board the NASA Earth Observing System (EOS) Aura satellite, and continues with new instruments, such as the Ozone Mapping and Profiler Suite (OMPS) on board the NASA-NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite. In this study, we demonstrate that it is possible, using our state-of-the-art principal component analysis (PCA) retrieval technique, to continue the long-term global SO2 pollution monitoring started by OMI with the current and future OMPS instruments that will fly on the NOAA Joint Polar Satellite System (JPSS) 1, 2, 3, and 4 satellites in addition to SNPP, with a very good consistency of retrievals from these instruments. Since OMI SO2 data have been primarily used for (1) providing regional context on air pollution and long-range transport on a daily basis and (2) providing information on point emission sources on an annual basis after data averaging, we focused on these two aspects in our OMI-OMPS comparisons. Four years of retrievals (2012-2015) have been compared for three regions: eastern China, Mexico, and South Africa. In general, the comparisons show relatively high correlations (r = 0. 79-0.96) of daily regional averaged SO2 mass between the two instruments and near-unity regression slopes (0.76-0.97). The annual averaged SO2 loading differences between OMI and OMPS are small (< 0.03 Dobson unit (DU) over South Africa and up to 0.1 DU over eastern China). We also found a very good correlation (r = 0. 92-0.97) in the spatial distribution of annual averaged SO2 between OMI and OMPS over the three regions during 2012-2015. The emissions from ∼ 400 SO2 sources calculated with the two instruments also show a very good correlation (r = ∼ 0.9) in each year during 2012-2015. OMPS-detected SO2 point source emissions are slightly lower than those from OMI, but OMI-OMPS differences decrease with increasing strength of source. The OMI-OMPS SO2 mass differences on a pixel by pixel (daily) basis in each region can show substantial differences. The two instruments have a spatial correlation coefficient of 0.7 or better on < ∼ 50 % of the days. It is worth noting that consistent SO2 retrievals were achieved without any explicit adjustments to OMI or OMPS radiance data and that the retrieval agreement may be further improved by introducing a more comprehensive Jacobian lookup table than is currently used.

satellite remote sensing of pollution↗
NASA NTRS2017

Daily Ambient Air Pollution Metrics for Five Cities: Evaluation of Data Fusion-Based Estimates and Uncertainties

Spatiotemporal characterization of ambient air pollutant concentrations is increasingly relying on the combination of observations and air quality models to provide well-constrained, spatially and temporally complete pollutant concentration fields. Air quality models, in particular, are attractive, as they characterize the emissions, meteorological, and physiochemical process linkages explicitly while providing continuous spatial structure. However, such modeling is computationally intensive and has biases. The limitations of spatially sparse and temporally incomplete observations can be overcome by blending the data with estimates from a physically and chemically coherent model, driven by emissions and meteorological inputs. We recently developed a data fusion method that blends ambient ground observations and chemical transport-modeled (CTM) data to estimate daily, spatially resolved pollutant concentrations and associatedcorrelations. In this study, we assess the ability of the data fusion method to produce daily metrics (i.e., 1-hr max, 8-hr max, and 24-hr average) of ambient air pollution that capture spatiotemporal air pollution trends for 12 pollutants (CO, NO2, NOx, O3, SO2, PM (sub10), PM (sub 2.5), and five PM (sub 2.5) components) across five metropolitan areas (Atlanta, Birmingham, Dallas, Pittsburgh, and St. Louis), from 2002 to 2008. Three sets of comparisons are performed: (1) the CTM concentrations are evaluated for each pollutant and metropolitan domain, (2) the data fusion concentrations are compared with the monitor data, (3) a comprehensive cross-validation analysis against observed data evaluates the quality of the data fusion model simulations across multiple metropolitan domains. The resulting daily spatial field estimates of air pollutant concentrations and uncertainties are not only consistent with observations, emissions, andmeteorology, but substantially improve CTM-derived results for nearly all pollutants and all cities, with the exception of NO2 for Birmingham. The greatest improvements occur for O3 and PM (sub 2.5). Squared spatiotemporal correlation coefficients range between simulations and observations determined using cross-validation across all cities for air pollutants of secondary and mixed origins are R-squared equal to 0.88-0.93 (O3), 0.81-0.89 (SO4), 0.67-0.83 (PM (sub 2.5)), 0.52-0.72 (NO3), 0.43-0.80 (NH4), 0.32-0.51 (OC), and 0.14-0.71 (PM (sub 10)). Results for relatively homogeneous pollutants of secondary origin, tend to be better than those for more spatially heterogeneous (larger spatial gradients) pollutants of primary origin (NOx, CO, SO2 and EC). Generally, background concentrations and spatial concentration gradients reflect interurban airshed complexity and the effects of regional transport, whereas daily spatial pattern variability shows intra urban consistency in the fused data. With sufficiently high CTM spatial resolution, traffic-related pollutants exhibit gradual concentration gradients that peak toward the urban centers. Ambient pollutant concentration uncertainty estimates for the fused data are both more accurate and smaller than those for either the observations or the model simulations alone.

spatiotemporal fusion↗
NASA NTRS2017

THEMIS Satellite Observations of Hot Flow Anomalies at Earth's Bow Shock

Hot flow anomalies (HFAs) at Earth's bow shock were identified in Time History of Events and Macroscale Interactions During Substorms (THEMIS) satellite data from 2007 to 2009. The events were classified as young or mature and also as regular or spontaneous hot flow anomalies (SHFAs). The dataset has 17 young SHFAs, 49 mature SHFAs, 15 young HFAs, and 55 mature HFAs. They span a wide range of magnetic local times (MLTs) from approximately 7 to 16.5MLT. The largest ratio of solar wind to HFA core density occurred near dusk and at larger distances from the bow shock. In this study, HFAs and SHFAs were observed up to 6.3 RE and 6.1 RE (Earth radii), respectively, upstream from the model bow shock. HFA-SHFA occurrence decreases with distance upstream from the bow shock. HFAs of the highest event core ion temperatures were not seen at the flanks. The ratio of HFA ion temperature increase to HFA electron temperature increase is highest around 12MLT and slightly duskward. For SHFAs, (Tihfa=Tisw)/(Tehfa=Tesw) generally increased with distance from the bow shock. Both mature and young HFAs are more prevalent when there is an approximately radial interplanetary magnetic field. HFAs occur most preferentially for solar wind speeds from 550 to 600 km s-1. The correlation coefficient between the HFA increase in thermal energy density from solar wind values and the decrease in kinetic energy density from solar wind values is 0.62. SHFAs and HFAs do not show major differences in this study.

Interplanetary physics (planetary bow shocks; sola↗
NASA NTRS2018

Estimation of Lower-Body Kinetics from Loading Profile and Kinematics Alone, Without Measured Ground Reaction Forces

Biomechanical models of human motion can estimate kinetic outcomes, such as joint moments, joint forces and muscle forces. Typically, one performs an inverse dynamics (ID) analysis to compute joint moments from joint angles and measured external forces. Sometimes it is impractical to measure ground reaction forces and moments (GRF&M). We devised an empirical method for performing ID analysis of resistance exercises without measured GRF&M. The method solves the multibody dynamics equations of motion with four key assumptions about the GRF&M that reduce the number of unknowns. The assumptions are 1) negligible ground reaction moments, 2) fixed lateral/medial location of the center of pressure (COP), 3) equal fore/aft location of the COP between the feet, and 4) constant angle of the GRF vector relative to the vertical axis in the frontal plane. We used evaluation trials from a spaceflight countermeasure resistance training device to test this approach. Four participants performed squat and deadlift exercises at various loads. We compared results from traditional ID analysis to results without measured GRF&M using our method. We found that joint moment trajectories in the sagittal plane were qualitatively similar in shape between the two methods, and the amount of root mean squared error (RMSE), measured by difference in joint moment impulse, was typically under 10 percent. Non-sagittal joint moment trajectories, which are much lower in overall magnitude, were not qualitatively similar in shape between the two methods. Non-sagittal moments displayed much higher RMSE, with typical values well over 50 percent. These findings were further supported by validation metrics (Sprague and Geers' P and M metrics, Pearson's r correlation coefficient). Based on these findings, we concluded that useful kinetic results are obtained from ID analysis of squat and deadlift exercises, even when GRF&M are not measured, as long as the outcomes of interest lie in the sagittal plane.

computational modeling↗
NASA NTRS2018

Seasonal and Interannual Assessment of Cloud Cover and Atmospheric Constituents Across the Amazon (2000 - 2015): Insights for Remote Sensing and Climate Analysis

The quantitative assessment of cloud cover and atmospheric constituents improves our ability to exploit the climate feedback into the Amazon basin. In the 21st century, three droughts have already occurred in the Amazonia (e.g. 2005, 2010, 2015), inducing regional changes in the seasonal patterns of atmospheric constituents. In addition to climate, the atmospheric dynamic and attenuation properties are long-term challenges for satellite-based remote sensing of this ecosystem: high cloudiness, abundant water vapor content and biomass burning season. Therefore, while climatology analysis supports the understanding of atmospheric variability and trends, it also offers valuable insights for remote sensing applications. In this study, we evaluate the seasonal and interannual variability of cloud cover and atmospheric constituents (aerosol loading, water vapor and ozone content) over the Amazon basin, with focus on both climate analysis and remote sensing implications. We take the advantage of new atmosphere daily products at 1 km resolution derived from Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm developed for Moderate Resolution Imaging Spectroradiometer (MODIS) data. An intercomparison of Aerosol Robotic Network (AERONET) and MAIAC aerosol optical depth (AOD) and columnar water vapor (CWV) showed quantitative information with a correlation coefficient higher than 0.81. Our results show distinct regional patterns of cloud cover across the Amazon basin: northwestern region presets a persistent cloud cover (> 80%) throughout the year, while low cloud cover (0-20%) occurs in the southern Amazon during the dry season. The cloud-free period in the southern Amazon is followed by an increase in the atmospheric burden due to fire emissions. Our results reveal that AOD records are changing in terms of area and intensity. During the 2005 and 2010 droughts, the positive AOD anomalies (δ > 0.1) occurred over 39.03% (240.3 million ha) and 27.14% (165.99 million ha) of total basin in the SON season, respectively. In contrast, the recent 2015 drought occurred towards the end of year (October through December) and these anomalies were observed over 23.72% (145 million ha) affecting areas in the central and eastern Amazon unlike previous droughts. The water vapor presents high concentration values (4.0-5.0 g/sq.cm) in the wet season (DJF), while we observed a strong spatial gradient from northwestern to southeastern of the basin during the dry season. In addition, we also found a positive trend of water vapor content ( ∼0.3 g/sq.cm) between 2000 and 2015. The total ozone typically varies between 220 and 270 DU, and it has a seasonal change of ∼25-35 DU from wet season to dry season caused by large emissions of ozone precursors and long-range transport. Finally, while this study contributes to climatological analysis of atmospheric constituents, the remote sensing users can also understand the regional constraints caused by atmospheric attenuation, such as high aerosol loading and cloud obstacles for surface observations.

ozone↗
NASA NTRS2018

Reliability of the Danish Aerospace Corporation Portable Pulmonary Function System

Metabolic gas analysis is a critical component of investigations that measure cardio-pulmonary exercise responses during and after long-duration spaceflight. The primary purpose of the current study was to determine the reliability and intra-subject repeatability of a metabolic gas analysis device, the Portable Pulmonary Function System (PPFS), designed for use on the International Space Station (ISS). The second objective of this study was to directly compare PPFS measurements of expired oxygen and carbon dioxide (FEO2 and FECO2) to values obtained from a well-validated clinical metabolic gas analysis system (ParvoMedics TrueOne (c) [PM]). Eight subjects performed four peak cycle tests to maximal exertion. The first test was used to prescribe work rates for the subsequent test sessions. Metabolic gas analysis for this test was performed by the PM, but samples of FEO2 and FECO2 also were simultaneously collected for analysis by the PPFS. Subjects then performed three additional peak cycle tests, consisting of three 5-min stages designed to elicit 25%, 50%, and 75% maximal oxygen consumption (VO2max) followed by stepwise increases of 25 W/min until subjects reached volitional exhaustion. Metabolic gas analysis was performed using the PPFS for these tests. Intraclass correlation coefficients (ICC), within-subject standard deviations (WS SD), and coefficients of variation (CV%) were calculated for the repeated exercise tests. Mixed model regression analysis was used to compare paired FEO2 and FECO2 values obtained from the PPFS and the PM during the initial test. The ICC values for oxygen consumption (VO2), carbon dioxide production (VCO2), and ventilation (VE) indicate that the PPFS is highly reliable (0.79 to 0.99) for all exercise levels tested; however, ICCs for respiratory exchange ratio (RER) were low ( 0.11 - 0.51), indicating poor agreement between trials during submaximal and maximal exercise. Overall, CVs ranged from 1.6% to 6.7% for all measurements, a finding consistent with reported values that were obtained using other metabolic gas analysis techniques. The PPFS and PM produced comparable FEO2 data; however, there was less agreement between measures of FECO2 obtained from the two devices, particularly at lower CO2 concentrations. The PPFS appears, in practically all respects, to yield highly reliable metabolic gas analysis data. Lower reliability of RER measurements reported in the literature and likely is not a function of the PPFS device. Further examination of PPFS CO2 data is warranted to better understand the limitations of these PPFS measurements. Overall, the PPFS when used for repeated measures of cardio-pulmonary exercise should provide accurate and reliable data for studies of human adaptation to spaceflight.

Portable Pulmonary Function System (PPFS)↗
NASA NTRS2017

Development and Assessment of the SMAP Enhanced Passive Soil Moisture Product

Launched in January 2015, the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) observatory was designed to provide frequent global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using a radar and a radiometer operating at L-band frequencies. Despite a hardware mishap that rendered the radar inoperable shortly after launch, the radiometer continues to operate nominally, returning more than two years of science data that have helped to improve existing hydrological applications and foster new ones.Beginning in late 2016 the SMAP project launched a suite of new data products with the objective of recovering some high-resolution observation capability loss resulting from the radar malfunction. Among these new data products are the SMAP Enhanced Passive Soil Moisture Product that was released in December 2016, followed by the SMAPSentinel-1 Active-Passive Soil Moisture Product in April 2017.This article covers the development and assessment of the SMAP Level 2 Enhanced Passive Soil Moisture Product (L2_SM_P_E). The product distinguishes itself from the current SMAP Level 2 Passive Soil Moisture Product (L2_SM_P) in that the soil moisture retrieval is posted on a 9 km grid instead of a 36 km grid. This is made possible by first applying the Backus-Gilbert optimal interpolation technique to the antenna temperature (TA) data in the original SMAP Level 1B Brightness Temperature Product to take advantage of the overlapped radiometer footprints on orbit. The resulting interpolated TA data then go through various correctioncalibration procedures to become the SMAP Level 1C Enhanced Brightness Temperature Product (L1C_TB_E). The L1C_TB_E product, posted on a 9 km grid, is then used as the primary input to the current operational SMAP baseline soil moisture retrieval algorithm to produce L2_SM_P_E as the final output. Images of the new product reveal enhanced visual features that are not apparent in the standard product. Based on in situ data from core validation sites and sparse networks representing different seasons and biomes all over the world, comparisons between L2_SM_P_E and in situ data were performed for the duration of April 1, 2015 October 30, 2016. It was found that the performance of the enhanced 9 km L2_SM_P_E is equivalent to that of the standard 36 km L2_SM_P, attaining a retrieval uncertainty below 0.040 m(exp 3)/m(exp 3) unbiased root-mean-square error (ubRMSE) and a correlation coefficient above 0.800. This assessment also affirmed that the Single Channel Algorithm using the V-polarized TB channel (SCA-V) delivered the best retrieval performance among the various algorithms implemented for L2_SM_P_E, a result similar to a previous assessment for L2_SM_P.

Enhanced↗
NASA NTRS2018

Prediction Skill of the MJO Teleconnection Signals in the NASA GEOS Subseasonal Reforecasts

Tropical-extratropical teleconnections are considered key to advancing subseasonal prediction. The Madden Julian oscillation (MJO), characterized by large scale convective envelopes propagating along the tropical Indo-Pacific sector, is known to modulate midlatitude circulation and associated weather patterns. Although there is a general consensus on the MJO's influence on the midlatitude circulation, which is thought to be due to modulations of the North Atlantic Oscillation (NAO) and the Pacific North America (PNA) pattern, relatively less is known about the predictability of these teleconnection signals in dynamical forecast models. The composite evolution of the midlatitude circulation anomalies and associated wave train structure as the delayed response to tropical heating are reported in many studies that have examined reanalyses and long model simulations. However, it is yet to be determined whether they lend any beneficial subseasonal forecast skill, especially to weekly mean surface temperature and precipitation over North America. Investigating useful predictable signals from the MJO teleconnections is also complicated by the fact that the MJO is a moving heat source with an approximate periodicity of 30-60 days, and that the structure and amplitude of the midlatitude response can be sensitive to the longitudinal positioning of the heating anomaly as well as the propagation speed of the MJO. The objective of this study is to investigate the impact of MJO teleconnections on forecast accuracy at 2-3 week lead over North America, with an emphasis on the above-mentioned lesser known aspects of these teleconnections. To this end, we utilize a suite of subseasonal reforecasts performed with the latest NASA GEOS-5 seasonal-to-subseasonal (S2S) system. These reforecasts were performed as part of the NOAA SubX project, wherein the NASA GEOS-5 atmosphere-ocean coupled model was run at degree horizontal resolution, initialized every 5 days for the period 1999-2016. The GEOS-5 model shows skillful predictions of the MJO, with the correlation coefficient based on the real-time multivariate MJO (RMM) index staying at or above 0.5 up to forecast lead 26-36 days. The system is thus a useful tool for investigating MJO teleconnection processes.

Achuthavarier, Deepthi↗
NASA NTRS2018

Solar Cycle Variations in Mesospheric Carbon Monoxide

As an extension of Lee et al. (2013), solar cycle variation of carbon monoxide (CO) is analyzed with MLS observation, which covers more than thirteen years (2004-2017) including maximum of solar cycle 24. Being produced primarily by the carbon dioxide (CO2) photolysis in the lower thermosphere, the variations of the mesospheric CO concentration are largely driven by the solar cycle modulated ultraviolet (UV) variation. This solar signal extends down to the lower altitudes by the dynamical descent in the winter polar vortex, showing a time lag that is consistent with the average descent velocity. To characterize a global distribution of the solar impact, MLS CO is correlated with the SORCE measured total solar irradiance (TSI) and UV. As high as 0.8 in most of the polar mesosphere, the linear correlation coefficients between CO and UV/TSI are more robust than those found in the previous work. The photochemical contribution explains most (68%) of the total variance of CO while the dynamical contribution accounts for 21% of the total variance at upper mesosphere. The photochemistry driven CO anomaly signal is extended in the tropics by vertical mixing. The solar cycle signal in CO is further examined with the Whole Atmosphere Community Climate Model (WACCM) 3.5 simulation by implementing two different modeled Spectral Solar Irradiances (SSIs): SRPM 2012 and NRLSSI. The model simulations underestimate the mean CO amount and solar cycle variations of CO, by a factor of 3, compared to those obtained from MLS observation. Different inputs of the solar spectrum have small impacts on CO variation.

MLS↗
NASA NTRS2018

Evaluation of SMAP Downscaled Brightness Temperature Using SMAPEx-4/5 Airborne Observations

The Soil Moisture Active and Passive (SMAP) mission, launched by the National Aeronautics and Space Administration (NASA) on 31st January 2015, was designed to provide global soil moisture every 2 to 3 days at 9 km resolution by downscaling SMAP passive microwave observations obtained at 36 km resolution using active microwave observations at 3 km resolution, and then retrieving soil moisture from the resulting 9 km brightness temperature product. This study evaluated the SMAP Active/Passive (AP) downscaling algorithm together with other resolution enhancement techniques. Airborne passive microwave observations acquired at 1 km resolution over the Murrumbidgee River catchment in south-eastern Australia during the fourth and fifth Soil Moisture Active Passive Experiments (SMAPEx-4/5) were used as reference data. The SMAPEx-4/5 data were collected in May and September 2015, respectively, and aggregated to 9 km for direct comparison with a number of available resolution-enhanced brightness temperature estimates. The results show that the SMAP AP downscaled brightness temperature had a correlation coefficient (R) of 0.84 and Root-Mean-Squared Error (RMSE) of ~10 K, while SMAP Enhanced, Nearest Neighbour, Weighted Average, and the Smoothing Filter-based Modulation (SFIM) brightness temperature estimates had somewhat better performance (RMSEs of ~7 K and an R exceeding 0.9). Although the SFIM had the lowest unbiased RMSE of ~6 K, the effect of cloud cover on Ka-band observations limits data availability.

downscaled brightness temperature↗
NASA NTRS2019

Updated MISR Over-Water Research Aerosol Retrieval Algorithm - Part 2: A Multi-Angle Aerosol Retrieval Algorithm for Shallow, Turbid, Oligotrophic, and Eutrophic Waters

Coastal waters serve as transport pathways to the ocean for all agricultural and other runoff from terrestrial sources, and many are the sites for upwelling of nutrient rich, deep water; they are also some of the most biologically productive on Earth. Estimating the impact coastal waters have on the global carbon budget requires relating satellite-based remote-sensing retrievals of biological productivity (e.g., chlorophyll alpha concentration) to in situ measurements taken in near-surface waters. The Multi-angle Imaging SpectroRadiometer (MISR) can uniquely constrain the "atmospheric correction" needed to derive ocean color from remote-sensing imagers. Here, we retrieve aerosol amount and type from MISR over all types of water. The primary limitation is an upper bound on aerosol optical depth (AOD), as the algorithm must be able to distinguish the surface. This updated MISR research aerosol retrieval algorithm (RA) also assumes that light reflection by the underlying ocean surface is Lambertian. The RA computes the ocean surface reflectance (R (sub rs)) analytically for a given AOD, aerosol optical model, and wind speed. We provide retrieval examples over shallow, turbid, and eutrophic waters and introduce a productivity and turbidity index (PTI), calculated from retrieved spectral R (sub rs), that distinguished water types (similar to the the normalized difference vegetation index, NDVI, over land). We also validate the new algorithm by comparing spectral AOD and Angstrom exponent (ANG) results with 2419 collocated AErosol RObotic NETwork (AERONET) observations. For AERONET 558 nanometer-interpolated AOD less than 1.0, the root-mean square error (RMSE) is 0.04 and linear correlation coefficient is 0.95. For the 502 cloud-free MISR and AERONET collocations with an AERONET AOD greater than 0.20, the ANG RMSE is 0.25 and r is 0.89. Although MISR RA AOD retrieval quality does not appear to be substantially impacted by the presence of turbid water, the MISR-RA-retrieved Angstrom exponent seems to suffer from increased uncertainty under such conditions. MISR supplements current ocean color sources in regions where sunglint precludes retrievals from single-view-angle instruments. MISR atmospheric correction should also be more robust than that derived from single-view instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). This is especially true in regions of shallow, turbid, and eutrophic waters, locations where biological productivity can be high, and single-view-angle retrieval algorithms struggle to separate atmospheric from oceanic features.

Satellite Remote Sensing↗
NASA NTRS2017

PM2.5 Variation Related to AOD and Meteorological Conditions on Diurnal Scale in Beijing, China

PM2.5 (particulate matter with diameter less than 2.5um) is one of the major air pollutants in many regions around the globe. Column integrated aerosol optical depth (AOD) measurement provides a potential way to estimate surface PM2.5 concentration from the remote sensing technics. In this analysis, we compiled hourly measurements of surface PM2.5 and AOD made in Beijing, China and meteorological conditions provided by NASA Global Modeling and Assimilation Office's Modern-Era Retrospective Analysis for Research and Applications (MERRA) product in recent years. On daily basis, hourly PM2.5 and AOD show positive correlation, i.e. correlation coefficient (R) is higher than 0.5 on about 50% of days. We apply multi-variant analysis to this dataset and estimate the magnitude of AOD variance that can be explained by or related to PM2.5 and relevant meteorological conditions changes.

PM2.5↗
NASA NTRS2018

A New Global Anthropogenic SO2 Emission Inventory for the Last Decade: A Mosaic of Satellite-Derived and Bottom-Up Emissions

Sulfur dioxide (SO2) measurements from the Ozone Monitoring Instrument (OMI) satellite sensor have been used to detect emissions from large point sources. Emissions from over 400 sources have been quantified individually based on OMI observations, accounting for about a half of total reported anthropogenic SO2 emissions. Here we report a newly developed emission inventory, OMI-HTAP, by combining these OMI-based emission estimates and the conventional bottom-up inventory, HTAP, for smaller sources that OMI is not able to detect. OMI-HTAP includes emissions from OMI-detected sources that are not captured in previous leading bottom-up inventories, enabling more accurate emission estimates for regions with such missing sources. In addition, our approach offers the possibility of rapid updates to emissions from large point sources that can be detected by satellites. Our methodology applied to OMI-HTAP can also be used to merge improved satellite-derived estimates with other multi-year bottom-up inventories, which may further improve the accuracy of the emission trends. OMI-HTAP SO2 emissions estimates for Persian Gulf, Mexico, and Russia are 59%, 65%, and 56% larger than HTAP estimates, respectively, in year 2010. We have evaluated the OMI-HTAP inventory by performing simulations with the Goddard Earth Observing System version 5 (GEOS-5) model. The GEOS-5 simulated SO2 concentrations driven by both HTAP and OMI-HTAP were compared against in situ measurements. We focus for the validation on year 2010 for which HTAP is most valid and for which a relatively large number of in situ measurements are available. Results show that the OMI-HTAP inventory improves the agreement between the model and observations, in particular over the US, with the normalized mean bias decreasing from 0.41 (HTAP) to -0.03 (OMI-HTAP) for year 2010. Simulations with the OMI-HTAP inventory capture the worldwide major trends of large anthropogenic SO2 emissions that are observed with OMI. Correlation coefficients of the observed and modelled surface SO2 in 2014 increase from 0.16 (HTAP) to 0.59 (OMI-HTAP) and the normalized mean bias dropped from 0.29 (HTAP) to 0.05 (OMI-HTAP), when we updated 2010 HTAP emissions with 2014 OMI-HTAP emissions in the model.

Sulfur dioxide (SO2)↗
NASA NTRS2019

Global Validation of Columnar Water Vapor Derived from EOS MODIS-MAIAC Algorithm Against the Ground-Based AERONET Observations

The water vapor is a relevant greenhouse gas in the Earth's climate system, and satellite products become one of the most effective way to characterize and monitor the columnar water vapor (CWV) content at global scale. Recently, a new product (MCD19) was released as part of MODIS (Moderate Resolution Imaging Spectroradiometer) Collection 6 (C6). This operational product from the Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm includes a high 1-kilometer resolution CWV retrievals. This study presents the first global validation of MAIAC C6 CWV obtained from MODIS MCD19A2 product. This evaluation was performed using Aerosol Robotic Network (AERONET) observations at 265 sites (2000-2017). Overall, the results show a good agreement between MAIAC/AERONET CWV retrievals, with correlation coefficient higher than 0.95 and RMS (Root Mean Square) error lower than 0.250 centimeters. The binned error analysis revealed an underestimation (approximately 10 percent) of Aqua CWV retrievals with negative bias for CWV higher than 3.0 centimeters. In contrast, Terra CWV retrievals show a slope of regression close to unity and a low mean bias of 0.075 centimeters. While the accuracy is relatively similar between 1.0 and 5.0 centimeters for both sensor products, Terra dataset is more reliable for applications in humid tropical areas (less than 5.0 centimeters). The expected error was defined as plus or minus 15 percent, with less than 68 percent of retrievals falling within this envelope. However, the accuracy is regionally dependent, and lower error should be expected in some regions, such as South America and Oceania. Since MODIS instruments have exceeded their design lifetime, time series analysis was also presented for both sensor products. The temporal analysis revealed a systematic offset of global average between Terra and Aqua CWV records. We also found an upward trend (approximately 0.2 centimeters per decade) in Terra CWV retrievals, while Aqua CWV retrievals remain stable over time. The sensor degradation influences the ability to detect climate signals, and this study indicates the need for revisiting calibration of the MODIS bands 17-19, mainly for Terra instrument, to assure the quality of the MODIS water vapor product. Finally, this study presents a comprehensive validation analysis of MAIAC CWV over land, raising the understanding of its overall quality.

MAIAC↗
NASA NTRS2019

Evaluating the Uncertainty of Terrestrial Water Budget Components over High Mountain Asia

This study explores the uncertainties in terrestrial water budget estimation over High Mountain Asia (HMA) using a suite of uncoupled land surface model (LSM) simulations. The uncertainty in the water balance components of precipitation (P), evapotranspiration (ET), runoff (R), and terrestrial water storage (TWS) is significantly impacted by the uncertainty in the driving meteorology, with precipitation being the most important boundary condition. Ten gridded precipitation datasets along with a mix of model-, satellite-, and gauge-based products, are evaluated first to assess their suitability for LSM simulations over HMA. The datasets are evaluated by quantifying the systematic and random errors of these products as well as the temporal consistency of their trends. Though the broader spatial patterns of precipitation are generally well captured by the datasets, they differ significantly in their means and trends. In general, precipitation datasets that incorporate information from gauges are found to have higher accuracy with low Root Mean Square Errors and high correlation coefficient values. An ensemble of LSM simulations with selected subset of precipitation products is then used to produce the mean annual fluxes and their uncertainty over HMA in P, ET, and R to be 2.11 ± 0.45, 1.26 ± 0.11, and 0.85 ± 0.36 mm per day, respectively. The mean annual estimates of the surface mass (water) balance components from this model ensemble are comparable to global estimates from prior studies. However, the uncertainty/spread of P, ET, and R is significantly larger than the corresponding estimates from global studies. A comparison of ET, snow cover fraction, and changes in TWS estimates against remote sensing-based references confirms the significant role of the input meteorology in influencing the water budget characterization over HMA and points to the need for improving meteorological inputs.

Terrestrial water budget↗
NASA NTRS2019

Evaluation Analysis of NASA SMAP L3 and L4 and SPoRT-LIS Soil Moisture Data in the United States

Soil moisture has a critical role in the development, frequency and persistence of climatic and hydrologic extremes such as drought, heat wave and flooding events. In situ soil moisture data are uneven and sparse in time and space. This highlights the need to utilize other soil moisture sources to fill this spatiotemporal gap. The goal of this study is to validate one satellite-based and two model-based soil moisture datasets with in situ data across the United States. Soil moisture information from the Soil Moisture Active Passive (SMAP) enhanced level 3 (L3) (SMAP L3) and modeled level 4 (L4) (SMAP L4) data at 9-km resolution and Short-term Prediction Research and Transition-Land Information System (SPoRT-LIS) modeled at 3-km resolution were selected for evaluation. SPoRT-LIS is a near real-time, high resolution operational land analysis data. Ground-based data were obtained from the North American Soil Moisture Database (NASMD) for 362 stations. Seven statistical indicators including anomaly, Spearman, and Pearson correlation coefficients, the systematic error (Bias), root mean square error (RMSE), unbiased root mean square error (ubRMSE), and normalized standard deviation (SDV) were used to evaluate the satellite- and model-derived soil moisture data. In addition, the triple collocation (TC) error model was used to measure the error among SMAP L4, SPoRT-LIS and ground-based data. This study assesses which satellite or modeled dataset is most appropriate for specific times and locations to use as a surrogate for in situ observations. Temporal and spatial analysis demonstrated that, overall, SMAP L4 performed better than SMAP L3 and SPoRT-LIS. Strong agreement was observed between SMAP L4 and in situ observations (ρ = 0.53, Bias = −0.006) in all seasons and most regions with various land covers, especially in winter and in the central regions of the United States. For croplands, SMAP L4 presented the best agreement with in situ data, analyzing all period (ρ = 0.60) and non-winter period (ρ = 0.61) separately.

Tavakol, Ameneh↗