nasa-gesdisc/es-publications-researchareas huggingface dataset:
A Bayesian framework for deriving sector-based methane emissions from top-down fluxes (2021)

Atmospheric methane observations are used to test methane emission inventories as the sum of emissions should correspond to observed methane concentrations. Typically, concentrations are inversely projected to a net flux through an atmospheric chemistry-transport model. Current methods to partition net fluxes to underlying sector-based emissions often scale fluxes based on the relative weight of sectors in a prior inventory. However, this approach imposes correlation between emission sectors which may not exist. Here we present a Bayesian optimal estimation method that projects inverse methane fluxes directly to emission sectors while accounting uncertainty structure and spatial resolution of prior fluxes and emissions. We apply this method to satellite-derived fluxes over the U.S. and at higher resolution over the Permian Basin to demonstrate that we can characterize a sector-based emission budget. This approach provides more robust comparisons between different top-down estimates, critical for assessing the efficacy of policies intended to reduce emissions.

Research areas: Greenhouse Gases
The Effect of GOSAT Observations on Estimates of Net CO2 Flux in Semi-Arid Regions of the Southern Hemisphere (2016)

Greenhouse gases Observing SATellite (GOSAT) is the operational satellite dedicated to atmospheric CO2 observations. Assimilation of data provided by GOSAT is expected to yield reliable CO2 fluxes in semi-arid regions because of frequent observations owing to clear skies. Here we estimated net CO2 flux over semi-arid regions of the Southern Hemisphere using the GOSAT column averaged CO2 (XCO2) and surface CO2 measurements. Assimilation of GOSAT XCO2 indicated that semi-arid regions are integral components of recent terrestrial CO2 uptake, accounting for 44% globally. Compared with estimates assimilated from surface measurements, estimates by GOSAT XCO2 suggest a 50% reduction in the semi-arid CO2 uptake, amounting to 1.1 Pg C yr1. Significant estimation differences occurred for South America and South Africa, where the GOSAT makes frequent measurements but where surface CO2 measurements are limited. In comparison, the two estimates varied less in Australia, where more surface measurements are available. These results suggest that GOSAT XCO2 is effective at regulating excess estimates of semi-arid CO2 uptake in regions that are less constrained by surface CO2 measurements. To promote understanding of climate change effects in semi-arid regions, it is important to continue monitoring trends in CO2 uptake with GOSAT.

Research areas: Greenhouse Gases
Stratospheric gravity waves over the mountainous island of South Georgia: testing a high-resolution dynamical model with 3-D satellite observations and … (2021)

Abstract. Atmospheric gravity waves (GWs) play an important role in atmospheric dynamics but accurately representing them in general circulation models (GCMs) is challenging. This is especially true for orographic GWs generated by wind flow over small mountainous islands in the Southern Ocean. Currently, these islands lie in the grey zone of global model resolution, where they are neither fully resolved nor fully parameterised. It is expected that as GCMs approach the spatial resolution of current high-resolution local-area models, small-island GW sources may be resolved without the need for parameterisations. But how realistic are the resolved GWs in these high-resolution simulations compared to observations? Here, we test a high-resolution (1.5 km horizontal grid, 118 vertical levels) local-area configuration of the Met Office Unified Model over the mountainous island of South Georgia (54 S, 36 W), running without GW parameterisations. The island's orography is well resolved in the model, and real-time boundary conditions are used for two time periods during July 2013 and JuneJuly 2015. We compare simulated GWs in the model to coincident 3-D satellite observations from the Atmospheric Infrared Sounder (AIRS) on board Aqua. By carefully sampling the model using the AIRS resolution and measurement footprints (denoted as model sampled as AIRS hereafter), we present the first like-for-like comparison of simulated and observed 3-D GW amplitudes, wavelengths and directional GW momentum flux (GWMF) over the island using a 3-D S-transform method. We find that the timing, magnitude and direction of simulated GWMF over South Georgia are in good general agreement with observations, once the AIRS sampling and resolution are applied to the model. Area-averaged zonal GWMF during these 2 months is westward at around 5.3 and 5.6 mPa in AIRS and model sampled as AIRS datasets respectively, but values directly over the island can exceed 50 mPa. However, up to 35 % of the total GWMF in AIRS is actually found upwind of the island compared to only 17 % in the model sampled as AIRS, suggesting that non-orographic GWs observed by AIRS may be underestimated in our model configuration. Meridional GWMF results show a small northward bias (20 %) in the model sampled as AIRS that may correspond to a southward wind bias compared to coincident radiosonde measurements. Finally, we present one example of large-amplitude (T1520 K at 45 km altitude) GWs at short horizontal wavelengths (H3040 km) directly over the island in AIRS measurements that show excellent agreement with the model sampled as AIRS. This suggests that orographic GWs in the full-resolution model with T45 K and H3040 km can occur in reality. Our study demonstrates that not only can high-resolution local-area models simulate realistic stratospheric GWs over small mountainous islands but the application of satellite sampling and resolution to these models can also be a highly effective method for their validation.

Research areas: Validation
Evaluation of ACOLITE atmospheric correction methods for Landsat-8 and Sentinel-2 in the Río de la Plata turbid coastal waters (2022)

Landsat-8 (L8) and Sentinel-2 (S2) terrestrial satellite missions have shown to contain useful information for aquatic applications. However, quantitative retrieval of water quality parameters, such as turbidity, strongly depend on the performance of atmospheric correction algorithms. Among available processors, ACOLITE (https://odnature.naturalsciences.be/remsem/software-and-data/acolite) is simple to incorporate in imagery processing routines and has shown to have better performance than other processors for sediment-rich waters. Recently (in 2018), it incorporated a new default atmospheric correction approach, the dark spectrum fit (DSF), which remains to be tested in most of the Southern hemisphere coastal areas. In this work, we present new in-situ radiometric measurements collected in the northern coast of the Rio de la Plata estuary, South America, during field campaigns along a 2-year period. The data set was used to evaluate the performance of ACOLITEs DSF and exponential extrapolation (EXP) methods with L8 and S2 imagery, and to recalibrate a turbidity algorithm. The DSF did not perform very well, giving particularly poor results in the near infrared (NIR) bands. However, its performance was greatly improved with an optional sun glint correction (DSF+GC), although some positive bias was still present in the NIR bands. A GC seemed to be most important in dates with higher sun elevation (austral spring and summer), and should be strongly considered for other water bodies in the region and in similar or lower latitudes (35S). Additionally, the EXP method gave good results in the green-NIR spectral region when a low (5th) percentile aerosol type was selected. Finally, the effect of the atmospheric correction on turbidity retrieval from satellite imagery was assessed: if the red and a NIR band were combined, the effect of the bias in the NIR region was negligible for the DSF+GC method; however, some impact was noticed for the lowest turbidity levels if a single NIR band was used.

Research areas: Validation
Large Uncertainty in Estimating pCO<inf>2</inf> From Carbonate Equilibria in Lakes (2017)

Most estimates of carbon dioxide (CO<SUB>2</SUB>) evasion from freshwaters rely on calculating partial pressure of aquatic CO<SUB>2</SUB> (pCO<SUB>2</SUB>) from two out of three CO<SUB>2</SUB>-related parameters using carbonate equilibria. However, the pCO<SUB>2</SUB> uncertainty has not been systematically evaluated across multiple lake types and equilibria. We quantified random errors in pH, dissolved inorganic carbon, alkalinity, and temperature from the North Temperate Lakes Long-Term Ecological Research site in four lake groups across a broad gradient of chemical composition. These errors were propagated onto pCO<SUB>2</SUB> calculated from three carbonate equilibria, and for overlapping observations, compared against uncertainties in directly measured pCO<SUB>2</SUB>. The empirical random errors in CO<SUB>2</SUB>-related parameters were mostly below 2% of their median values. Resulting random pCO<SUB>2</SUB> errors ranged from ±3.7% to ±31.5% of the median depending on alkalinity group and choice of input parameter pairs. Temperature uncertainty had a negligible effect on pCO<SUB>2</SUB>. When compared with direct pCO<SUB>2</SUB> measurements, all parameter combinations produced biased pCO<SUB>2</SUB> estimates with less than one third of total uncertainty explained by random pCO<SUB>2</SUB> errors, indicating that systematic uncertainty dominates over random error. Multidecadal trend of pCO<SUB>2</SUB> was difficult to reconstruct from uncertain historical observations of CO<SUB>2</SUB>-related parameters. Given poor precision and accuracy of pCO<SUB>2</SUB> estimates derived from virtually any combination of two CO<SUB>2</SUB>-related parameters, we recommend direct pCO<SUB>2</SUB> measurements where possible. To achieve consistently robust estimates of CO<SUB>2</SUB> emissions from freshwater components of terrestrial carbon balances, future efforts should focus on improving accuracy and precision of CO<SUB>2</SUB>-related parameters (including direct pCO<SUB>2</SUB>) measurements and associated pCO<SUB>2</SUB> calculations.

Research areas: Validation
Assessment of the EUMETSAT Multi Decadal Land Surface Albedo Data Record (2021)

Surface albedo, defined as the ratio of the surface-reflected irradiance to the incident irradiance, is one of the parameters driving the Earth energy budget and it is for this reason an essential variable in climate studies. Instruments on geostationary satellites provide suitable observations allowing long-term monitoring of surface albedo from space. In 2012, EUMETSAT published Release 1 of the Meteosat Surface Albedo (MSA) data record. The main limitation effecting the quality of this release was non-removed clouds by the incorporated cloud screening procedure that caused too high albedo values, in particular for regions with permanent cloud coverage. For the generation of Release 2, the MSA algorithm has been replaced with the Geostationary Surface Albedo (GSA) one, able to process imagery from any geostationary imager. The GSA algorithm exploits a new, improved, cloud mask allowing better cloud screening, and thus fixing the major limitation of Release 1. Furthermore, the data record has an extended temporal and spatial coverage compared to the previous release. Both Black-Sky Albedo (BSA) and White-Sky Albedo (WSA) are estimated, together with their associated uncertainties. A direct comparison between Release 1 and Release 2 clearly shows that the quality of the retrieval improved significantly with the new cloud mask. For Release 2 the decadal trend is less than 1% over stable desert sites. The validation against Moderate Resolution Imaging Spectroradiometer (MODIS) and the Southern African Regional Science Initiative (SAFARI) surface albedo shows a good agreement for bright desert sites and a slightly worse agreement for urban and rain forest locations. In conclusion, compared with MSA Release 1, GSA Release 2 provides the users with a significantly more longer time range, reliable and robust surface albedo data record.

Research areas: Validation
Feasibility of a global inversion for spatially resolved glacial isostatic adjustment and ice sheet mass changes proven in simulation experiments (2022)

Estimating mass changes of ice sheets or of the global ocean from satellite gravimetry strongly depends on the correction for the glacial isostatic adjustment (GIA) signal. However, geophysical GIA models are different and incompatible with observations, particularly in Antarctica. Regional inversions have resolved GIA over Antarctica without ensuring global consistency, while global inversions have been mostly constrained by a priori GIA patterns. For the first time, we set up a global inversion to simultaneously estimate ice sheet mass changes and GIA, where Antarctic GIA is spatially resolved using a set of global GIA patterns. The patterns are related to deglaciation impulses localized along a grid over Antarctica. GIA associated with four regions outside Antarctica is parametrized by global GIA patterns induced by deglaciation histories. The observations we consider here are satellite gravimetry, satellite altimetry over Antarctica and Greenland, as well as modelled firn thickness changes. Firn thickness changes are also parametrized to account for systematic errors in their modelling. Results from simulation experiments using realistic signals and error covariances support the feasibility of the approach. For example, the spatial RMS error of the estimated Antarctic GIA effect, assuming a 10-year observation period, is 31% and 51%, of the RMS of two alternative global GIA models. The integrated Antarctic GIA error is 8% and 5%, respectively, of the integrated GIA signal of the two models. For these results realistic error covariances incorporated in the parameter estimation process are essential. If error correlations are neglected, the Antarctic GIA RMS error is more than twice as large.Highlights$$\bullet $$We present a globally consistent inversion approach to co-estimate glacial isostatic adjustment effects together with changes of the ice mass and firn air content in Greenland and Antarctica. $$\bullet $$The inversion method utilizes data sets from satellite gravimetry, satellite altimetry, regional climate modelling, and firn modelling together with the full error-covariance information of all input data. $$\bullet $$The simulation experiments show that the proposed GIA parametrization in Antarctica can resolve GIA effects unpredicted by geophysical modelling, despite realistic input-data limitations.

Research areas: Validation
Evaluation of the MODIS (C6) Daily Albedo Products for Livingston Island, Antarctic (2021)

Although extensive research of Moderate Resolution Imaging Spectroradiometer (MODIS) albedo data is available on the Greenland Ice Sheet, there is a lack of studies evaluating MODIS albedo products over Antarctica. In this paper, MOD10A1, MYD10A1, and MCD43 (C6) daily albedo products were compared with the in situ albedo data on Livingston Island, South Shetland Islands (SSI), Antarctica, from 2006 to 2015, for both all-sky and clear-sky conditions, and for the entire study period and only the southern summer months. This is the first evaluation in which MYD10A1 and MCD43 are also included, which can be used to improve the accuracy of the snow BRDF/albedo modeling. The best correlation was obtained with MOD10A1 in clear-sky conditions (r = 0.7 and RMSE = 0.042). With MCD43, only data from the backup algorithm could be used, so the correlations obtained were lower (r = 0.6). However, it was found that there was no significant difference between the values obtained for all-sky and for clear-sky data. In addition, the MODIS products were found to describe the in situ data trend, with increasing albedo values in the range between 0.04 decade1 and 0.16 decade1. We conclude that MODIS daily albedo products can be applied to study the albedo in the study area.

Research areas: Validation
Validation of GSMaP Products for a Heavy Rainfall Event over Complex Terrain in Mongolia Captured by the GPM Core Observatory (2021)

This paper focuses on the uncertainty of summer precipitation estimations produced by Global Satellite Mapping of Precipitation (GSMaP) over Mongolia, a region that has complex terrain and sparse weather observation networks. We first compared average summer precipitation over Mongolian territory as reported by several precipitation products. Although the interannual variability of the product was comparable, the amount of recorded precipitation differed among the various products. The rain-gauge-based analysis reported the lowest amount of precipitation, while the satellite-based GSMaP_MVK reported the highest amount. Our results represent a first estimate of the characteristic differences among the various precipitation-monitoring products, including GPM-based products, as they relate to climatic and hydro-meteorological assessments in Mongolia. We then made a detailed comparison using a case study in which a heavy rainfall event was captured by the Global Precipitation Measurement (GPM) mission's core observatory near Ulaanbaatar in July 2016. In this case, gauged and ungauged GSMaP estimates of the precipitation over the mountain area differed substantially between algorithm versions 6 and 7. An intercomparison of atmospheric numerical modeling, the GPM core observatory, and rain gauge observation showed that the rain gauge calibration of GSMaP effectively moderates the large error of the ungauged GSMaP data. The source of the significant ungauged GSMaP error is likely to be the rain rate estimates in version 7 of the algorithm. However, GSMaP gauge-calibrated estimates of the precipitation over mountainous areas may be affected by a potential underestimation of gauge analysis due to the missing localized precipitation occurring in the large gaps of the routine observation network. We expect that these findings will be helpful for developers seeking to further improve the GSMaP algorithm.

Research areas: Validation
Multi-objective calibration of MIKE SHE with SMAP soil moisture datasets (2019)

Root zone soil moisture plays an important role in water storage in hydrological processes. The recently launched Soil Moisture Active Passive (SMAP) mission has produced a high-resolution assimilation product of global root zone soil moisture that can be applied to improve the performance of hydrological models. In this study, we compare three calibration approaches in the Beimiaoji watershed. The first approach is single-objective calibration, in which only observed streamflow is used as a benchmark for comparison with the other approaches. The second and third approaches use multi-objective calibration based on SMAP root zone soil moisture and observed streamflow. The difference between the second and third approaches is the metric used to characterize the root zone soil moisture. The second approach applies the mean, which was commonly used in previous studies, whereas the third approach applies the hydrologic complexity , a dimensionless metric based on information entropy theory. These approaches are implemented to calibrate the distributed hydrological model MIKE SHE. Results show that the root zone soil moisture simulation is clearly improved, whereas streamflow simulation suffers from a slightly negative impact with multi-objective calibration. The hydrologic complexity  performs better than the mean in capturing the features of root zone soil moisture.

Research areas: Validation
ICESat-2 laser data denoising algorithm based on a back propagation neural network (2022)

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) photon data is the emerging satellite-based LiDAR data, widely used in surveying and mapping due to its small photometric spot and high density. Since ICESat-2 data collect weak signals, it is difficult to denoise in shallow sea island areas, and the quality of the denoising method will directly affect the precision of bathymetry. This paper proposes a back propagation (BP) neural network-based denoising algorithm for the data characteristics of shallow island reef areas. First, a horizontal elliptical search area is constructed for the photons in the dataset. Suitable feature values are selected in the search area to train the BP neural network. Finally, data with a geographic location far apart, including daily and nightly data, are selected respectively for experiments to test the generality of the network. By comparing the results with the confidence labels provided in the official documents of the ATL03 dataset, the DBSCAN algorithm, and the manual visual interpretation, it is proved that the denoising algorithm proposed in this paper has a better processing effect in shallow island areas.

Research areas: Validation
Components of 21 years (1995–2015) of absolute sea level trends in the Arctic (2022)

Abstract. The Arctic Ocean is at the frontier of the fast-changing climate in the northern latitudes, and sea level trends are a bulk measure of ongoing processes related to climate change. Observations of sea level in the Arctic Ocean are nonetheless difficult to validate with independent measurements, and this is globally the region where the sea level trend (SLT) is most uncertain. The aim of this study is to create a satellite-independent reconstruction of Arctic SLT, as it is observed by altimetry and tide gauges (TGs). Previous studies use Gravity Recovery and Climate Experiment (GRACE) observations to estimate the manometric (mass component of) SLT. GRACE estimates, however, are challenged by large mass changes on land, which are difficult to separate from much smaller ocean mass changes. Furthermore, GRACE is not available before 2003, which significantly limits the period and makes the trend more vulnerable to short-term changes. As an alternative approach, this study estimates the climate-change-driven Arctic manometric SLT from the Arctic sea level fingerprints of glaciers, Greenland, Antarctica and glacial isostatic adjustment (GIA) with the addition of the long-term inverse barometer (IB) effect. The halosteric and thermosteric components complete the reconstructed Arctic SLT and are estimated by interpolating 300 000 temperature (T) and salinity (S) in situ observations. The SLT from 19952015 is compared to the observed SLT from altimetry and 12 selected tide gauges (TGs) corrected for vertical land movement (VLM). The reconstructed estimate manifests the salinity-driven halosteric component as dominating the spatial SLT pattern with variations between 7 and 10 mm yr1. The manometric SLT in comparison is estimated to be 12 mm yr1 for most of the Arctic Ocean. The reconstructed SLT shows a larger sea level rise in the Beaufort Sea compared to altimetry, an issue that is also identified by previous studies. There is a TG-observed sea level rise in the Siberian Arctic in contrast to the sea level fall from the reconstructed and altimetric estimate. From 19952015 the reconstructed SLT agrees within the 68 % confidence interval with the SLT from observed altimetry in 87 % of the Arctic between 65 N and 82 N (R=0.50) and with 5 of 12 TG-derived (VLM-corrected) SLT estimates. The residuals are seemingly smaller than results from previous studies using GRACE estimates and modeled TS data. The spatial correlation of the reconstructed SLT to altimetric SLT during the GRACE period (20032015) is R=0.38 and R=0.34/R=0.37 if GRACE estimates are used instead of the constructed manometric component. Thus, the reconstructed manometric component is suggested as a legitimate alternative to GRACE that can be projected into the past and future.

Research areas: Validation, Atmospheric/Ocean Indicators