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NASA NTRS2020

Surface melting over the Greenland ice sheet from enhanced resolution passive microwave brightness temperatures (1979–2019)

Surface melting is a major component of the Greenland ice sheet (GrIS) surface mass balance, affecting sea level rise through direct runoff and the modulation on ice dynamics and hydrological processes, supraglacially, englacially and subglacially. Passive microwave (PMW) brightness temperature observations are of paramount importance in studying the spatial and temporal evolution of surface melting in view of their long temporal coverage (1979–to date) and high temporal resolution (daily). However, a major limitation of PMW datasets has been the relatively coarse spatial resolution, being historically of the order of tens of kilometres. Here, we use a newly released passive microwave dataset (37 GHz, horizontal polarization) made available through the NASA MeASUREs program to study the spatiotemporal evolution of surface melting over the GrIS at an enhanced spatial resolution of 3.125 Km. We assess the outputs of different detection algorithms through data collected by Automatic Weather Stations (AWS) and the outputs of the MAR regional climate model. We found that surface melting is well captured using a dynamic algorithm based on the outputs of MEMLS model, capable to detect sporadic and persistent melting. Our results indicate that, during the reference period 1979–2019 (1988–2019), surface melting over the GrIS increased in terms of both duration, up to ~4.5 (2.9) days per decade, and extension, up to 6.9 % (3.6 %) of the GrIS surface extent per decade, according to the MEMLS algorithm. Furthermore, the melting season has started up to ~4 (2.5) days earlier and ended ~7 (3.9) days later per decade. We also explored the information content of the enhanced resolution dataset with respect to the one at 25 km and MAR outputs through a semi-variogram approach. We found that the enhanced product is more sensitive to local scale processes, hence confirming the potential interest of this new enhanced product for studying surface melting over Greenland at a higher spatial resolution than the historical products and monitor its impact on sea level rise. This offers the opportunity to improve our understanding of the processes driving melting, to validate modelled melt extent at high resolution and potentially to assimilate this data in climate models.

Surface melting↗
NASA NTRSDate not supplied

Quantifying the benefits of improved satellite remote-sensing observations for inverse modeling of NOx and NMVOC emissions

This study aims to demonstrate the benefits of using novel high spatiotemporal retrieval products from newer satellites for top-down emission estimates of nitrogen oxides (NO x ) and non-methane volatile organic compounds (NMVOCs) for the summer of 2019 over the contiguous United States. Recent satellite retrievals have not only advanced spatiotemporal resolution but also greatly reduced error and uncertainty due to reduced noise in the retrievals compared to spaceborne sensors launched in the past. We applied inverse modeling techniques using tropospheric nitrogen dioxide (NO 2 ) and formaldehyde (H-CHO) column retrieval products from the Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) in conjunction with the Weather Research Forecast and Community Multiscale Air Quality Modeling system (WRF-CMAQ). In order to provide a better representation of background chemical composition and avoid misalignment of emission adjustment, we applied monthly scaling factors for ozone (O 3 ) and CO boundary concentrations in addition to the inclusion of lightning and aviation emissions. Satellite-constrained NO x and NMVOCs posterior emissions showed a mitigated discrepancy between observed and modeled columns. The improvement in the model performance was greater when using TROPOMI, primarily benefiting from reduced errors/biases of the satellite retrievals that enabled us to explore corresponding changes in O 3 concentrations and production sensitivity regimes using the ratio of H-CHO and NO 2 .

remote sensing↗
NASA NTRS2011

Bridging Empirical and Physical Approaches for Landslide Monitoring and Early Warning

Rainfall-triggered landslides typically occur and are evaluated at local scales, using slope-stability models to calculate coincident changes in driving and resisting forces at the hillslope level in order to anticipate slope failures. Over larger areas, detailed high resolution landslide modeling is often infeasible due to difficulties in quantifying the complex interaction between rainfall infiltration and surface materials as well as the dearth of available in situ soil and rainfall estimates and accurate landslide validation data. This presentation will discuss how satellite precipitation and surface information can be applied within a landslide hazard assessment framework to improve landslide monitoring and early warning by considering two disparate approaches to landslide hazard assessment: an empirical landslide forecasting algorithm and a physical slope-stability model. The goal of this research is to advance near real-time landslide hazard assessment and early warning at larger spatial scales. This is done by employing high resolution surface and precipitation information within a probabilistic framework to provide more physically-based grounding to empirical landslide triggering thresholds. The empirical landslide forecasting tool, running in near real-time at http://trmm.nasa.gov, considers potential landslide activity at the global scale and relies on Tropical Rainfall Measuring Mission (TRMM) precipitation data and surface products to provide a near real-time picture of where landslides may be triggered. The physical approach considers how rainfall infiltration on a hillslope affects the in situ hydro-mechanical processes that may lead to slope failure. Evaluation of these empirical and physical approaches are performed within the Land Information System (LIS), a high performance land surface model processing and data assimilation system developed within the Hydrological Sciences Branch at NASA's Goddard Space Flight Center. LIS provides the capabilities to quantify uncertainty from model inputs and calculate probabilistic estimates for slope failures. Results indicate that remote sensing data can provide many of the spatiotemporal requirements for accurate landslide monitoring and early warning; however, higher resolution precipitation inputs will help to better identify small-scale precipitation forcings that contribute to significant landslide triggering. Future missions, such as the Global Precipitation Measurement (GPM) mission will provide more frequent and extensive estimates of precipitation at the global scale, which will serve as key inputs to significantly advance the accuracy of landslide hazard assessment, particularly over larger spatial scales.

Kirschbaum, Dalia↗
NASA NTRS2021

Surface Melting Over the Greenland Ice Sheet Derived From Enhanced Resolution Passive Microwave Brightness Temperatures (1979–2019)

Surface melting is a major component of the Greenland ice sheet surface mass balance, and it affects sea level rise through direct runoff and the modulation of ice dynamics and hydrological processes, supraglacially, englacially and subglacially. Passive microwave (PMW) brightness temperature observations are of paramount importance in studying the spatial and temporal evolution of surface melting due to their long temporal coverage (1979–present) and high temporal resolution (daily). However, a major limitation of PMW datasets has been the relatively coarse spatial resolution, which has historically been of the order of tens of kilometers. Here, we use a newly released PMW dataset (37 GHz, horizontal polarization) made available through a NASA “Making Earth System Data Records for Use in Research Environments” (MeASUREs) program to study the spatiotemporal evolution of surface melting over the Greenland ice sheet at an enhanced spatial resolution of 3.125 km. We assess the outputs of different detection algorithms using data collected by automatic weather stations (AWSs) and the outputs of the Modèle Atmosphérique Régional (MAR) regional climate model. We found that sporadic melting is well captured using a dynamic algorithm based on the outputs of the Microwave Emission Model of Layered Snowpack (MEMLS), whereas a fixed threshold of 245 K is capable of detecting persistent melt. Our results indicate that, during the reference period from 1979 to 2019 (from 1988 to 2019), surface melting over the ice sheet increased in terms of both duration, up to 4.5 (2.9) d per decade, and extension, up to 6.9 % (3.6 %) of the entire ice sheet surface extent per decade, according to the MEMLS algorithm. Furthermore, the melting season started up to 4.0 (2.5) d earlier and ended 7.0 (3.9) d later per decade. We also explored the information content of the enhanced-resolution dataset with respect to the one at 25 km and MAR outputs using a semi-variogram approach. We found that the enhanced product is more sensitive to local-scale processes, thereby confirming the potential of this new enhanced product for monitoring surface melting over Greenland at a higher spatial resolution than the historical products and for monitoring its impact on sea level rise. This offers the opportunity to improve our understanding of the processes driving melting, to validate modeled melt extent at high resolution and, potentially, to assimilate these data in climate models.

Surface melting↗
NASA NTRS2015

Spatiotemporal Coherence of Mean and Extreme Aerosol Particle Events over Eastern North America as Observed from Satellite

Quantifying spatiotemporal scales of coherence and variability of aerosol particle properties provides (i) metrics for evaluating observational data sets and model simulations, (ii) inferences regarding dominant causes of variability, and (iii) information that is necessary to determine their climate forcing and air quality impacts. Accordingly, analyses of columnar aerosol optical depth (AOD) and Ångstrom exponent (AE) from the MODerate resolution Imaging Spectroradiometer (MODIS) on the Aqua and Terra satellites, and AErosol RObotic NETwork (AERONET) stations are used to evaluate the spatially averaged (Level-3) MODIS products, and to characterize the spatiotemporal scales of variability (and coherence) of aerosol particles over eastern North America (2000-2013). Results show a high degree of consistency in AOD retrievals from the different data sets but larger discrepancies in AE estimates, highest mean AOD and lowest day-to-day variability during the summer, and largest scales of spatial coherence in summer and fall. Consistent with the scales of spatial coherence from MODIS data, in both data sets power spectra indicate AOD variability is manifest primarily on synoptic and annual time scales. Conversely, AE variability in MODIS data is primarily focused on seasonal, semiannual, and annual time scales, and there is an additional mode of AE variability at approx. 30 days in the AERONET measurements. The frequency of cooccurrence of extreme AOD values (>local 90th percentile) decreases to below 50% at approx. 150 km from a central grid cell, but is above that expected by random chance over almost all of eastern North America, indicating supra-regional scale extreme events.

MODIS↗
NASA NTRS2021

Examining Runner’s Outdoor Heat Exposure Using Urban Microclimate Modeling and GPS Trajectory Mining

It is important to quantify human heat exposure in order to evaluate and mitigate the negative impacts of heat on human well-being in the context of global warming. This study proposed a human-centric framework to examine human personal heat exposure based on anonymous GPS trajectories data mining and urban microclimate modeling. The mean radiant temperature (Tmrt) that represents the human body’s energy balance was used to indicate human heat exposure. The meteorological data and high-resolution 3D urban model generated from multispectral remotely sensed images and LiDAR data were used as inputs in urban microclimate modeling to map the spatio-temporal distribution of the Tmrt in the Boston metropolitan area. The anonymous human GPS trajectory data collected from fitness Apps was used to map the spatiotemporal distribution of human outdoor activities. By overlaying the anonymous GPS trajectories on the generated spatio-temporal maps of Tmrt, this study further examined the heat exposure of runners in different age-gender groups in the Boston area. Results show that there is no significant difference in terms of heat exposure for female and male runners. The female runners in the age of 45-54 are exposed to more heat than female runners of 18-24 and 25-34, while there is no significant difference among male runners. This study proposed a novel method to estimate human heat exposure, which would shed new light on mitigating the negative impacts of heat on human health.

Personal heat exposure↗
NASA NTRSDate not supplied

Evaluation of Planetary Boundary Layer Structure from NASA Global Modeling and Assimilation Office’s Next Retrospective Analysis Product MERRA-21C

The Planetary Boundary Layer (PBL) is a complex interface between Earth’s surface and the atmosphere with high spatiotemporal variability in its characteristics, and accurate simulation and observation of the PBL has proven to be a challenge. In this study, we evaluate PBL structures from NASA Global Modeling and Assimilation Office (GMAO)’s next retrospective analysis product: the Goddard Earth Observing System Retrospective Analysis of the early 21st Century (GEOS-R21C), with 25-km horizontal resolution. The GEOS-R21C incorporates a wide range of observing systems and various improvements over previous GMAO reanalysis. We compare the PBL thermodynamic structure as well as PBL Height (PBLH) from GEOS-R21C with various PBL observations. For PBLH, definitions are different depending on each observing system. PBLHs derived from radiosonde observations and Global Navigation Satellite System Radio Occultation (GNSS RO) are based on bulk Richardson number and refractivity gradient, respectively. The results of the evaluation of PBL structure from GMAO’s next retrospective analysis product will be presented. In addition, comparison between near-real-time (NRT) commercial and NASA commercial GNSS RO PBLH will also be discussed.

Eun-Gyeong Yang↗
NASA NTRS2022

A Framework for Deep Learning Emulation of Numerical Models With a Case Study in Satellite Remote Sensing

Numerical models based on physics represent the state of the art in Earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolutions overwhelms the latest generation computers, reducing the ability of modelers to generate simulations for understanding parameter sensitivities and characterizing variability and uncertainty. Thus, surrogate models are often developed to capture the essential attributes of the full-blown numerical models. Recent successes of machine learning methods, especially deep learning (DL), across many disciplines offer the possibility that complex nonlinear connectionist representations may be able to capture the underlying complex structures and nonlinear processes in Earth systems. A difficult test for DL-based emulation, which refers to function approximation of numerical models, is to understand whether they can be comparable to traditional forms of surrogate models in terms of computational efficiency while simultaneously reproducing model results in a credible manner. A DL emulation that passes this test may be expected to perform even better than simple models with respect to capturing complex processes and spatiotemporal dependencies. Here, we examine, with a case study in satellite-based remote sensing, the hypothesis that DL approaches can credibly represent the simulations from a surrogate model with comparable computational efficiency. Our results are encouraging in that the DL emulation reproduces the results with acceptable accuracy and often even faster performance. We discuss the broader implications of our results in light of the pace of improvements in high-performance implementations of DL and the growing desire for higher resolution simulations in the Earth sciences.

Bayesian Deep Learning↗
NASA NTRS2021

Assessment of Tropospheric Concentrations of NO2 from the TROPOMI/Sentinel-5 Precursor for the Estimation of Long-Term Exposure to Surface NO2 over South Korea

Since April 2018, the TROPOspheric Monitoring Instrument (TROPOMI) has provided data on tropospheric NO2 column concentrations (CTROPOMI) with unprecedented spatial resolution. This study aims to assess the capability of TROPOMI to acquire high spatial resolution data regarding surface NO2 mixing ratios. In general, the instrument effectively detected major and moderate sources of NO2 over South Korea with a clear weekday–weekend distinction. We compared the CTROPOMI with surface NO2 mixing ratio measurements from an extensive ground-based network over South Korea operated by the Korean Ministry of Environment (SKME; more than 570 sites), for 2019. Spatiotemporally collocated CTROPOMI and SKME showed a moderate correlation (correlation coefficient, r = 0.67), whereas their annual mean values at each site showed a higher correlation (r = 0.84). The CTROPOMI and SKME were well correlated around the Seoul metropolitan area, where significant amounts of NO2 prevailed throughout the year, whereas they showed lower correlation at rural sites. We converted the tropospheric NO2 from TROPOMI to the surface mixing ratio (STROPOMI) using the EAC4 (ECMWF Atmospheric Composition Reanalysis 4) profile shape, for quantitative comparison with the SKME. The estimated STROPOMI generally underestimated the in-situ value obtained, SKME (slope = 0.64), as reported in previous studies.

TROPOMI↗
NASA NTRS2019

Evaluation of CLARA-A2 and ISCCP-H Cloud Cover Climate Data Records over Europe with ECA&D Ground-Based Measurements

Clouds are of high importance for the climate system but they still remain one of its principal uncertainties. Remote sensing techniques applied to satellite observations have assisted tremendously in the creation of long-term and homogeneous data records; however, satellite data sets need to be validated and compared with other data records, especially ground measurements. In the present study, the spatiotemporal distribution and variability of Total Cloud Cover (TCC) from the Satellite Application Facility on Climate Monitoring (CM SAF) Cloud, Albedo And Surface Radiation dataset from AVHRR data—edition 2 (CLARA-A2) and the International Satellite Cloud Climatology Project H-series (ISCCP-H) is analyzed over Europe. The CLARA-A2 data record has been created using measurements of the Advanced Very High Resolution Radiometer (AVHRR) instrument onboard the polar orbiting NOAA and the EUMETSAT MetOp satellites, whereas the ISCCP-H data were produced by a combination of measurements from geostationary meteorological satellites and the AVHRR instrument on the polar orbiting satellites. An intercomparison of the two data records is performed over their common period, 1984 to 2012. In addition, a comparison of the two satellite data records is made against TCC observations at 22 meteorological stations in Europe, from the European Climate Assessment & Dataset (ECA&D). The results indicate generally larger ISCCP-H TCC with respect to the corresponding CLARA-A2 data, in particular in the Mediterranean. Compared to ECA&D data, both satellite datasets reveal a reasonable performance, with overall mean TCC biases of 2.1 and 5.2% for CLARA-A2 and ISCCP-H, respectively. This, along with the higher correlation coefficients between CLARA-A2 and ECA&D TCC, indicates the better performance of CLARA-A2 TCC data.

Tzallas, Vasileios↗
NASA NTRSDate not supplied

Deriving a Suite of Climate Data Records from 21-years of Sounder Observations

Two retrieval algorithms, a Single Field-of-view Sounder Atmospheric Product (SiFSAP) Level 2 algorithm and a Climate Fingerprinting Sounder Product (ClimFiSP) Level 3 algorithm, have been developed at NASA Langley Research Center. In this presentation, we will demonstrate the radiance closure build into these two algorithms. This is possible due to the use of a principal-component-based radiative transfer model (PCRTM). The PCRTM enables the inclusion of all available spectral information from hyperspectral IR sounders. It also has fast and accurate capability of modeling observed cloudy radiance spectra directly. The SiFSAP, which fits sounder radiance spectra using PCRTM as forward model and an optimal estimation as inverse model, has 3-times higher spatial resolution and 9-times denser data products comparing to current NASA and NOAA operational IR sounder products. With great support from NASA sounder SIPS, the SiFSAP algorithm has been delivered to NASA GES DISC for L2 and L3 data generation and releasing. The ClimFiSP uses a spectral fingerprinting method to directly generate L3 data products from spatiotemporally averaged L1 data. The goal of ClimFiSP is to generate high-quality climate data records (CDRs) from multiple IR sounders (e.g. Aqua AIRS, SNPP CrIS, NOAA-20 and NOAA-21 CrIS) using a consistent retrieval methodology. Using CHIRP L1 data, we have generated 20-years of CDRs for atmospheric temperature, water vapor, and trace gas profiles, as well as cloud and surface properties. The algorithm will be transitioned to Sounder SIPS soon and eventually to NASA GES DISC.

Xu Liu↗
NASA NTRS2018

The Dawn of Geostationary Air Quality Monitoring: Case Studies from Seoul and Los Angeles

With the near-future launch of geostationary pollution monitoring satellite instruments over North America, East Asia, and Europe, the air quality community is preparing for an integrated global atmospheric composition observing system at unprecedented spatial and temporal resolutions. One of the ways that NASA has supported this community preparation is through demonstration of future space-borne capabilities using the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument. This paper integrates repeated high-resolution maps from GeoTASO, ground-based Pandora spectrometers, and low Earth orbit measurements from the Ozone Mapping and Profiler Suite (OMPS), for case studies over two metropolitan areas: Seoul, South Korea on June 9, 2016 and Los Angeles, California on June 27, 2017. This dataset provides a unique opportunity to illustrate how geostationary air quality monitoring platforms and ground-based remote sensing networks will close the current spatiotemporal observation gap. GeoTASO observes large differences in diurnal behavior between these urban areas, with NO2 accumulating within the Seoul Metropolitan Area through the day but NO2 peaking in the morning and decreasing throughout the afternoon in the Los Angeles Basin. In both areas, the earliest morning maps exhibit spatial patterns similar to emission source areas (e.g., urbanized valleys, roadways, major airports). These spatial patterns change later in the day due to boundary layer dynamics, horizontal transport, and chemistry. The nominal resolution of GeoTASO is finer than will be obtained from geostationary platforms, but when NO2 data over Los Angeles are up-scaled to the expected resolution of TEMPO, spatial features discussed are conserved. Pandora instruments installed in both metropolitan areas capture the diurnal patterns observed by GeoTASO, continuously and over longer time periods, and will play a critical role in validation of the next generation of satellite measurement. These case studies demonstrate that different regions can have diverse diurnal patterns and that day-to-day variability due to meteorology or anthropogenic patterns such as weekday/weekend variations in emissions is large. Low Earth orbit measurements, despite their inability to capture the diurnal patterns at fine spatial resolution, will be essential for intercalibrating the geostationary radiances and cross-validating the geostationary retrievals in an integrated global observing system.

Geostationary↗
NASA NTRS2021

Recovery: Fast and Slow—Vegetation Response During the 2012–2016 California Drought

The 2012–2016 California Drought severely impacted natural vegetation across a wide range of environmental gradient. Although several studies have reported an increase in plant water stress and mortality, the spatiotemporal variations of ecosystem productivity responses and the associated environmental and biological drivers remain unclear. Here, using Enhanced Vegetation Index from the Moderate resolution imaging spectrometer, we found that 45% of the natural ecosystems showed an abrupt change (breakpoint [BP]) in productivity during 2012–2016. There were three major contrasting temporal patterns of productivity responses: (i) a steady increase under higher temperature followed by a decline due to accumulated moisture depletion (high elevation forest) or temperature decrease (high elevation nonforest), (ii) gradual decline during the drought followed by a rapid recovery within 1 year after drought stress was partially relieved, and (iii) both a gradual decline and an abrupt decline. The magnitude of abrupt changes was negatively correlated (r = −0.80, p < 0.001) with initial gradual changes. Overall, changes during BP offset, on average, 57% of the preceding gradual responses. The spatial variability in ecosystem response patterns is driven by both environmental and biological factors. Particularly, for forests, positive BP was driven by increasing rainfall and decreasing temperature, while negative BP was mainly driven by the precipitation anomaly. By 2019, 33% of the natural vegetation have recovered to the level of EVI in 2010. Ecosystem responses to multiyear droughts can influence ecosystem dynamics in a complex pattern. Multiple ecohydrological factors should be considered to understand and predict the long-term drought impacts on ecosystems.

Xi Yang↗
NASA NTRS2023

Spatiotemporal Characterization of Droughts and Vegetation Response in Northwest Africa From 1981 to 2020

Drought has become one of the most devastating natural risks of agricultural production and the environment in almost all climate regions. Thus, understanding the spatiotemporal characteristics of drought and its associated impacts is crucial in drought early warning management and adaptation efforts. In this study, we used the 3-month Standardized Precipitation Index (SPI-3) obtained from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) to investigate the space–time characteristics of drought conditions. Also, this study examined the impact of the SPI-based drought on vegetation health conditions using the Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) time-series data from 1981 to 2020. The results revealed that the region experiences drought primarily between July and September, with the most prolonged drought events lasting up to five months. Morocco suffered from more frequent droughts than other countries in the region. The Mann-Kendall test showed that the trend of drought became drier over the last decade, whereas the period from 1981 to 2010 witnessed either wetting or no trends. This study also found that the response of crops and grasslands showed higher correlation with the SPI-3 and that the response of vegetation to droughts was higher during the dry season. The findings of this study provide useful information to support local and regional drought planning and adaptation programs and enhance the understanding of drought development in the region.

Nguyen Quang Thi↗
NASA NTRSDate not supplied

Correlated Spatiotemporal Evolution of Extreme-Ultraviolet Ribbons and Hard X-rays in a Solar Flare

We study the structure and evolution of solar flare ribbons in the chromosphere to infer properties of magnetic reconnection that occurs in the corona. We analyze the imaging observations of the M7.3 SOL2014-04-18T13 flare obtained by IRIS in both the near and far ultraviolet passbands and by SDO/AIA in the 1600A passband. Two flare ribbons are observed to spread away from the magnetic polarity inversion line as the flare progresses. Using the high-resolution IRIS observations, we measure the width of the newly brightened ribbon front along the extension of the ribbon, which maps the feet of magnetic field lines reconnecting in the current sheet in the corona. We find that the width of the ribbon front is highly structured, possibly reflecting corresponding structure in the coronal current layer. Further the ribbon grows most rapidly with time in regions where non-thermal hard X-ray (HXR) emission is concentrated (Brosius, et al. 2015). The light curve of the ultraviolet emissions in this region, as measured by IRIS and AIA, also well matches the HXR light curve at photon energies above 25 keV. In contrast, the ribbon-width evolution and light curves in other regions along the ribbons do not correlate well with the HXR emission. These results suggest that there is a strong connection between the production of non-thermal electrons and locally enhanced perpendicular extent of flare ribbon fronts, which reflect the inhomogeneous structure and/or reconnection dynamics of the flare current sheet in the corona.

Stephen Naus↗
NASA NTRSDate not supplied

Generation of Land Surface Reflectance with Combined Geo-KOMPSAT-2A AMI and Himawari 8 AHI Observations

The latest generation of geostationary satellites has opened a new era of Earth observations with unprecedented spatiotemporal resolution and spectral range. Together with GOES 16/17 ABI, FY4-A AGRI, and Himawari-8 AHI, a new Korean geostationary satellite (Geo-KOMPSAT-2A AMI) has operationally collected a full-disk image in 16 channels every ten minutes since July 2019, allowing diurnal land surface monitoring over a large proportion of Asia and all of Oceania. Retrieving accurate surface reflectance (SR) over land from GK-2A/AMI is a challenging but high priority objective. One of the challenges is the absence of a spectral band in the 2.2 m SWIR range from AMI, which is required by many atmospheric correction algorithms to retrieve atmospheric aerosol properties. To remedy this issue, we adopt a strategy that combines concurrent GK-2A/AMI and Himawari 8/AHI observations in order to derive AMI SR. We have adapted the NASA Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to process the data stream from Himawari 8/AHI. The advantages of the MAIAC algorithm is its capability to exploit the high temporal frequency and varying illumination geometry of the geostationary data for advanced cloud/snow detection, aerosol retrieval, and characterization of surface bidirectional reflectance properties. Leveraging the similarities of spectral bands and the sun-target-sensor geometry between AMI and AHI, we are able to create denser time series of observations and enhanced BRDF samples over most of the spatial coverage of AMI (and AHI). The combined stereo-type observations not only help derive SR for AMI but also enhance retrievals of the corresponding AHI surface products. We evaluate the resulting AMI SR using ground (AERONET) observations and corresponding MODIS products. Further, we discuss potential challenges in utilizing the geostationary satellite data for land surface monitoring.

geostationary satellite↗
NASA NTRS2012

Characterization of Turbulent Latent and Sensible Heat Flux Exchange Between the Atmosphere and Ocean in MERRA

Turbulent fluxes of heat and moisture across the atmosphere-ocean interface are fundamental components of the Earth's energy and water balance. Characterizing both the spatiotemporal variability and the fidelity of these exchanges of heat and moisture is critical to understanding the global water and energy cycle variations, quantifying atmosphere-ocean feedbacks, and improving model predictability. This study examines the veracity of the recently completed NASA Modern-Era Retrospective analysis for Research and Applications (MERRA) product with respect to its representation of the surface turbulent heat fluxes. A validation of MERRA turbulent heat fluxes and near-surface bulk variables at local, high-resolution space and time scales is achieved by making comparisons to a large suite of direct observations. Both in situ and satellite-observed gridded surface heat flux estimates are employed to investigate the spatial and temporal variability of the surface fluxes with respect to their annual mean climatologies, their seasonal covariability of near-surface bulk parameters, and their representation of extremes. The impact of data assimilation on the near-surface parameters is assessed through evaluation of incremental analysis update tendencies produced by the assimilation procedure. It is found that MERRA turbulent surface heat fluxes are relatively accurate for typical conditions but have systematically weak vertical gradients in moisture and temperature and have a weaker covariability between the near-surface gradients and wind speed than found in observations. This results in an underestimate of the surface latent and sensible heat fluxes over the western boundary current and storm track regions. The assimilation of observations mostly acts to bring MERRA closer to observational products by increasing moisture and temperature near the surface and decreasing the near-surface wind speeds. The major patterns of spatial and temporal variability of the turbulent heat fluxes produced by MERRA compare favorably to observationally based estimates. However, MERRA is distinct in terms of amplitude. These results suggest that MERRA is likely to be a valuable resource for a number of research applications though, as with all turbulent flux estimates, systematic issues should be taken into account

Robert, J. Brent↗
NASA NTRS2012

Characterization of Turbulent Latent and Sensible Heat Flux Exchange Between the Atmosphere and Ocean in MERRA

Turbulent fluxes of heat and moisture across the atmosphere-ocean interface are fundamental components of the Earth s energy and water balance. Characterizing both the spatiotemporal variability and the fidelity of these exchanges of heat and moisture is critical to understanding the global water and energy cycle variations, quantifying atmosphere-ocean feedbacks, and improving model predictability. This study examines the veracity of the recently completed NASA Modern-Era Retrospective analysis for Research and Applications (MERRA) product with respect to its representation of the surface turbulent heat fluxes. A validation of MERRA turbulent heat fluxes and near-surface bulk variables at local, high-resolution space and time scales is achieved by making comparisons to a large suite of direct observations. Both in situ and satellite-observed gridded surface heat flux estimates are employed to investigate the spatial and temporal variability of the surface fluxes with respect to their annual mean climatologies, their seasonal covariability of near-surface bulk parameters, and their representation of extremes. The impact of data assimilation on the near-surface parameters is assessed through evaluation of incremental analysis update tendencies produced by the assimilation procedure. It is found that MERRA turbulent surface heat fluxes are relatively accurate for typical conditions but have systematically weak vertical gradients in moisture and temperature and have a weaker covariability between the near-surface gradients and wind speed than found in observations. This results in an underestimate of the surface latent and sensible heat fluxes over the western boundary current and storm track regions. The assimilation of observations mostly acts to bring MERRA closer to observational products by increasing moisture and temperature near the surface and decreasing the near-surface wind speeds. The major patterns of spatial and temporal variability of the turbulent heat fluxes produced by MERRA compare favorably to observationally based estimates. However, MERRA is distinct in terms of amplitude. These results suggest that MERRA is likely to be a valuable resource for a number of research applications though, as with all turbulent flux estimates, systematic issues should be taken into account.

Roberts, J. Brent↗