nasa-gesdisc/es-publications-researchareas huggingface dataset:
Atmospheric Correction of DSCOVR EPIC: Version 2 MAIAC Algorithm (2021)

The Earth Polychromatic Imaging Camera (EPIC) onboard the Deep Space Climate Observatory (DSCOVR) provides multispectral images of the sunlit disk of Earth since 2015 from the L1 orbit, approximately 1.5 million km from Earth toward the Sun. The NASAs Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has been adapted for DSCOVR/EPIC data providing operational processing since 2018. Here, we describe the latest version 2 (v2) MAIAC EPIC algorithm over land that features improved aerosol retrieval with updated regional aerosol models and new atmospheric correction scheme based on the ancillary bidirectional reflectance distribution function (BRDF) model of the Earth from MAIAC MODIS. The global validation of MAIAC EPIC aerosol optical depth (AOD) with AERONET measurements shows a significant improvement over v1 and the mean bias error MBE = 0.046, RMSE = 0.159, and
              R
              = 0.77. Over 66.7% of EPIC AOD retrievals agree with the AERONET AOD to within  (0.1 + 0.1AOD). We also analyze the role of surface anisotropy, particularly important for the backscattering view geometry of EPIC, on the result of atmospheric correction. The retrieved BRDF-based bidirectional reflectance factors (BRF) are found higher than the Lambertian reflectance by 815% at 443 nm and 12% at 780 nm for EPIC observations near the local noon. Due to higher uncertainties, the atmospheric correction at UV wavelengths of 340, 388 nm is currently performed using a Lambertian approximation.

Research areas: Validation
A Bayesian Fuzzy Clustering Approach for Design of Precipitation Gauge Network Using Merged Remote Sensing and Ground‐based Precipitation Products (2022)

A two-level clustering approach is proposed for optimal design/expansion of a ground-based precipitation monitoring network (GPN). It harnesses the advantages of Infinite Bayesian fuzzy clustering in the first level to partition the study area into homogeneous precipitation zones by considering structural/statistical characteristics and temporal variability of the observed precipitation. In the second level, an ensemble of hierarchical and partitional clustering techniques is considered in the time domain to effectively partition each zone into groups by considering weighted inter-site dissimilarities of precipitation. The dissimilarities account for correlation, temporal dynamics, and fuzzy mutual information of precipitation at existing stations and possible new gauge locations. Key station's location in each group is identified by a proposed ranking procedure that accounts for population density, land-use/landcover, and fuzzy marginal entropy of precipitation. For use with the approach, information on precipitation was derived for fine resolution ungauged grids covering the study area using random forest-based regression relationships developed for gauged grids between merged multiple satellite-based precipitation products (CHIRPS, IMERG) and ground-based precipitation measurements. The potential of the proposed approach over other clustering-based procedures is illustrated through a case study on a GPN comprising 1,128 gauges in Karnataka state (191,791 km<SUP>2</SUP>) of India. Potential locations for installing new gauges and areas where there is scope for relocating existing stations are identified. The proposed methodology appears promising and could be extended to design networks monitoring various other hydrometeorological variables.

Research areas: Validation
The RELAMPAGO lightning mapping array: Overview and initial comparison with the geostationary lightning mapper (2020)

Abstract During November 2018April 2019, an 11-station very high frequency (VHF) Lightning Mapping Array (LMA) was deployed to Cordoba Province, Argentina. The purpose of the LMA was validation of the Geostationary Lightning Mapper (GLM), but the deployment was coordinated with two field campaigns. The LMA observed 2.9 million flashes ( five sources) during 163 days, and level-1 (VHF locations), level-2 (flashes classified), and level-3 (gridded products) datasets have been made public. The networks performance allows scientifically useful analysis within 100 km when at least seven stations were active. Careful analysis beyond 100 km is also possible. The LMA dataset includes many examples of intense storms with extremely high flash rates (>1 s1), electrical discharges in overshooting tops (OTs), as well as anomalously charged thunderstorms with low-altitude lightning. The modal flash altitude was 10 km, but many flashes occurred at very high altitude (1520 km). There were also anomalous and stratiform flashes near 57 km in altitude. Most flashes were small (<50 km2 area). Comparisons with GLM on 14 and 20 December 2018 indicated that GLM most successfully detected larger flashes (i.e., more than 100 VHF sources), with detection efficiency (DE) up to 90%. However, GLM DE was reduced for flashes that were smaller or that occurred lower in the cloud (e.g., near 6-km altitude). GLM DE also was reduced during a period of OT electrical discharges. Overall, GLM DE was a strong function of thunderstorm evolution and the dominant characteristics of the lightning it produced.

Research areas: Validation
The Community Earth System Model Version 2 (CESM2) (2020)

An overview of the Community Earth System Model Version 2 (CESM2) is provided, including a discussion of the challenges encountered during its development and how they were addressed. In addition, an evaluation of a pair of CESM2 long preindustrial control and historical ensemble simulations is presented. These simulations were performed using the nominal 1° horizontal resolution configuration of the coupled model with both the "low-top" (40 km, with limited chemistry) and "high-top" (130 km, with comprehensive chemistry) versions of the atmospheric component. CESM2 contains many substantial science and infrastructure improvements and new capabilities since its previous major release, CESM1, resulting in improved historical simulations in comparison to CESM1 and available observations. These include major reductions in low-latitude precipitation and shortwave cloud forcing biases; better representation of the Madden-Julian Oscillation; better El Niño-Southern Oscillation-related teleconnections; and a global land carbon accumulation trend that agrees well with observationally based estimates. Most tropospheric and surface features of the low- and high-top simulations are very similar to each other, so these improvements are present in both configurations. CESM2 has an equilibrium climate sensitivity of 5.1-5.3 °C, larger than in CESM1, primarily due to a combination of relatively small changes to cloud microphysics and boundary layer parameters. In contrast, CESM2's transient climate response of 1.9-2.0 °C is comparable to that of CESM1. The model outputs from these and many other simulations are available to the research community, and they represent CESM2's contributions to the Coupled Model Intercomparison Project Phase 6.

Research areas: Validation
Diurnal Variation of Surface Wind Divergence in the Maritime Continent Using ASCAT and SeaWinds Observations and ERA5 Reanalysis Data (2022)

This study investigates the diurnal variation of surface wind divergence in the seas of the Maritime Continent by using satellite scatterometer observations and atmospheric reanalysis data. This is the first study to demonstrate the distribution and seasonal variation of the diurnally varying surface winds in the Maritime Continent in terms of wind divergence. Wind divergence develops from the coasts of the islands toward the center of the seas and dominates during the afternoon and evening hours. Wind convergence dominates over the seas during the nighttime and morning hours. The offshore extensions of the wind divergence and convergence from the coast differ regionally and thus show the asymmetric patterns with respect to the center of the seas. In particular, strong wind divergence develops from the southern coasts of the Java Sea and the Arafura Sea to extend northward beyond the center of the seas. The diurnal amplitudes of wind divergence vary seasonally and reach a peak in September in most of the seas. The switching times between wind divergence and convergence are almost fixed throughout the year regardless of the monsoon reversal.

Research areas: Atmospheric/Ocean Indicators
Blending drifters and altimetric data to estimate surface currents: Application in the Levantine Mediterranean and objective validation with different data types (2021)

An improved estimation of the surface currents in the Levantine Basin of the Mediterranean sea is crucial for a wide range of applications, including pollutants transport and nutrients distribution. This estimation remains challenging due to the scarcity or shortcomings of various data types used for this purpose. In this paper, we present an objective validation of a variational assimilation algorithm that blends geostrophic velocities derived from altimetry, wind-induced velocities, and drifter positions, to continuously obtain velocity corrections. The assessment of the validation impact was based on available independent in-situ data (current meters, gliders, and independent drifters) and satellite ocean color images. In all cases, the improvement was shown either qualitatively (position of the eddies) or quantitatively.

Research areas: Validation
Combining simulated hyperspectral EnMAP and Landsat time series for forest aboveground biomass mapping (2021)

Forest aboveground biomass (AGB) is a critical measure of ecosystem structure and plays a key role in global carbon cycling. Due to its widespread availability, optical remotely sensed data are key for regional- and global-scale AGB assessment, and with the planned and recent launches of spaceborne imaging spectroscopy missions such as the Environmental Mapping and Analysis Program (EnMAP), understanding the benefit of added spectral information for AGB mapping is important. We used simulated EnMAP imagery derived from Airborne Visible InfraRed Imaging Spectrometer (AVIRIS) imagery acquired over Sonoma County, California, USA in combination with Landsat time series to map forest AGB. A Gaussian Process Regression model was implemented to estimate forest AGB from one- and two-date (April and June) EnMAP imagery. A lidar-based reference AGB map was used as base data for training and validation sample extraction. As a comparison, we used corresponding Landsat Best Available Pixel (BAP) composites as well as a year-long 16-day interpolated Landsat time series (TS) for 2013. EnMAP imagery was able to effectively map forest AGB, with the two-date model (RMSE = 97.5 Mg/ha) outperforming the two single date models. All EnMAP models outperformed the corresponding Landsat BAP models, the best of which was the two-date model (RMSE = 108.8 Mg/ha). The added temporal dimension of the Landsat time series resulted in the best Landsat-based AGB map (RMSE = 102.3 Mg/ha). Combining the two datasets further improved AGB mapping efforts, with 2-date EnMAP + 2013 Landsat TS providing the best overall AGB maps (RMSE = 86.0 Mg/ha). This study demonstrates not only the added value of hyperspectral imagery for forest AGB mapping, but also the possible synergies between hyperspectral and multispectral data sources and hence between spectrally and temporally dense information. It can be expected that with the next generation of spaceborne hyperspectral sensors, the combination of dense spectral and temporal data will work to further improve global efforts for mapping forest AGB from optical Earth observation data.

Research areas: Land Surface/Agriculture Indicators, Validation
Towards a regional dust modeling system in the central Middle East: Evaluation, uncertainties and recommendations (2021)

This study aims towards an accurate regional dust modeling system in the central Middle East area (CME), through the implementation of the state-of-art dust parameterizations. The modeling system consists of the natural emission model NEMO, the meteorological model WRF and the chemistry transport model CAMx. An extensive evaluation of 16 different configurations has been realized, incorporating all the combinations of the components utilized in the state-of-art dust modeling approaches, namely the drag partition, the sandblasting efficiency, the horizontal mass flux, as well two commonly used soil particle size distributions. Daily mean PM10 measurements in Doha, as well the satellite AOD products of MODIS have been used for the quantitative and the qualitative assessment of the simulations. Noteworthy, each of these assessments did not yield to the exact same ranking of the configurations (e.g. best five) but they assisted on identifying clear patterns. For example, a consistent overestimation of the daily mean PM10, when the MacKinnon's drag partition scheme is utilized, was found. On the other hand, the assessments led to best three performing configurations, with common components the Raupach's drag partition scheme and Alfaro and Gomes sandblasting efficiency. Although their overall performance is good, several issues were found i.e. on individual dust events and a mean underestimation during the studied period, ranging from 49 to 75 g/m3. One cause of this underestimation could be the aerodynamic entrainment, a mechanism usually neglected from the dust modeling approaches. Another cause could be missed regional and/or local sources or underestimation of their emission rates.

Research areas: Air Quality, Validation
A unified framework of water balance models for monthly, annual, and mean annual timescales (2020)

At present, many conceptual water balance models have been proposed on monthly, annual, and mean annual timescales. With the increasing applications of these models, an emerging question is whether the water balance models on different timescales have some commonalities and can be integrated. To this end, this study established and applied a practical unified framework of water balance models that can be applied to different timescales. The framework was first developed on monthly timescale, where the water balance was captured by combining the abcd model and Budyko hypothesis with four parameters. Then, this framework was extended to annual and mean annual timescales under which certain parameters can be eliminated or assumed to be certain values. To be specific, on the annual timescale, the water balance was simulated by a Budyko-type model incorporating the water storage change; on the mean annual timescale the water balance was further simplified into a Budyko-type model which neglects the water storage change. Thus, all of the three typical water balance models on different timescales were unified to a general framework. The models were applied to 437 catchments in the contiguous United States with satisfying results achieved. Considering the water storage changes can improve the performance of annual water balance models in regions with clear interannual carrying water storage. Furthermore, the water balance models on different timescales share common supply and demand limits which is similar to Budyko hypothesis.

Research areas: Land Surface/Agriculture Indicators, Validation
Lake level and surface topography measured with spaceborne GNSS‐reflectometry from CYGNSS mission: Example for the lake Qinghai (2018)

This paper demonstrates inland water altimetry of spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) using the Cyclone GNSS (CYGNSS) mission data. From 12 tracks of raw data overpassing the Lake Qinghai, the bistatic group delay and carrier phase delay are extracted from the quasi-specular GNSS reflections. The water levels derived from the group delay observations are consistent with the Cryosat-2 and in situ gauge measurements. The surface topography profiles from the phase delay measurements show good self-consistence along the coincident tracks. Decimeter-level surface height anomalies are resolved with the phase delay measurements, which can be associated with the accuracy degradation of the geoid model in this region. Systematic errors remain in both group delay and phase delay altimetry measurements, which are attributed to the receiver orbit errors and ionospheric correction residuals. These limitations can be eliminated in further GNSS-R missions dedicated to altimetry applications.

Research areas: Validation
Coupled estimation of 500 m and 8-day resolution global evapotranspiration and gross primary production in 2002–2017 (2019)

Accurate quantification of terrestrial evapotranspiration (ET) is essential to understand the Earth's energy and water budgets under climate change. However, despite water and carbon cycle coupling, there are few diagnostic global evapotranspiration models that have complete carbon constraint on water flux run at a high spatial resolution. Here we estimate 8-day global ET and gross primary production (GPP) at 500 m resolution from July 2002 to December 2017 using a coupled diagnostic biophysical model (called PML-V2) that, built using Google Earth Engine, takes MODIS data (leaf area index, albedo, and emissivity) together with GLDAS meteorological forcing data as model inputs. PML-V2 is well calibrated against 8-day measurements at 95 widely-distributed flux towers for 10 plant functional types, indicated by Root Mean Square Error (RMSE) and Bias being 0.69 mm d1 and 1.8% for ET respectively, and being 1.99 g C m2 d1 and 4.2% for GPP. Compared to that performance, the cross-validation results are slightly degraded, with RMSE and Bias being 0.73 mm d1 and 3% for ET, and 2.13 g C m2 d1 and 3.3% for GPP, which indicates robust model performance. The PML-V2 products are noticeably better than most GPP and ET products that have a similar spatial resolution, and suitable for assessing the influence of carbon-induced impacts on ET. Our estimates show that global ET and GPP both significantly (p 

Research areas: Land Surface/Agriculture Indicators, Validation
A Topological Data Analysis approach for retrieving Local Climate Zones patterns in satellite data (2021)

In the context of geospatial studies, meaningful information may be hidden in the aspects of form and connectivity inscribed in the measurements. Therefore, here is proposed the use of H0 Persistent Homology (PH), a Topological Data Analysis tool to automatically summarize and quantify relevant spatial features in satellite data. With that aim, we extend the algebraic concepts of cubical complexes to the satellite data perspective and describe homology groups portrayal. As a proof by example, we present an inter-site comparison of Enhanced Vegetation Index from MODerate-resolution Imaging Spectroradiometer over fifteen regions worldwide. There, the Local Climate Zone (LCZ) framework is used to examine the outcomes of the PH filtration. Then, the features from every region that were encapsulated by the PH were compared against each other with the aid of the Bottleneck Distance metric. After that, it was performed a dimensionality reduction with a multi-dimensional scaling to build a 2-D geometry of the level of similarity among them. Thereby, the common aspects of the regions became explicit by their coordinates proximity in space. Then, with the use of the K-means algorithm, we were able to cluster those areas belonging to the same LCZ class. The results indicate that the proposed methods are robust to missing data in the satellite data and insensitive to a certain level of inhomogeneity in the spatial subsetting of data. Furthermore, the outcomes provide insights on several viable applications for future research.

Research areas: Land Surface/Agriculture Indicators
Monthly Surface Elevation Changes of the Greenland Ice Sheet From ICESat-1, CryoSat-2, and ICESat-2 Altimetry Missions (2022)

The Greenland Ice Sheet (GrIS) mass balance shows significant variabilities over a range of time scales. As geodetic records lengthen over time, it becomes insufficient to characterize the temporal evolution of the ice sheet by using a best-fit linear trend over a given observation period. This study investigates the joint analysis of laser and radar satellite altimeter measurements for estimating GrIS surface elevation changes (SECs) with a 30-day resolution. We first apply a crossover analysis to assess the precisions of the surface elevations measured by ICESat-1/2 laser altimeters and CryoSat-2 radar altimeter over the GrIS, which are needed for assigning weights for each data set in the joint analysis. Then, based on a modified repeat-track approach, we analyze the surface elevation measurements of ICESat-1/2 and CryoSat-2 to produce monthly SEC estimates for the past two decades, together with their associated uncertainties. The multimission SEC estimates are further assessed by using IceBridge airborne laser measurements, showing differences with a median value of 12 cm  60 cm. The monthly SEC time series reveal important variations over a range of time scales across different parts of the GrIS and would facilitate the investigation of complex spatiotemporal patterns of GrIS changes.

Research areas: Cryospheric Indicators, Validation
Multi-satellite retrieval of single scattering albedo using the OMI–MODIS algorithm (2019)

Abstract. Single scattering albedo (SSA) represents a unique identification of aerosol type and can be a determinant factor in the estimation of aerosol radiative forcing. However, SSA retrievals are highly uncertain due to cloud contamination and aerosol composition. The recent improvement in the SSA retrieval algorithm has combined the superior cloud-masking technique of the Moderate Resolution Imaging Spectroradiometer (MODIS) and the higher sensitivity of the Ozone Monitoring Instrument (OMI) to aerosol absorption. The combined OMIMODIS algorithm has only been validated over a small spatial and temporal scale. The present study validates the algorithm over global oceans for the period from 2008 to 2012. The geographical heterogeneity in the aerosol type and concentration over the Atlantic Ocean, the Arabian Sea and the Bay of Bengal was useful to delineate the effect of aerosol type on the retrieval algorithm. We also noted that OMI overestimated SSA when absorbing aerosols were present closer to the surface. We attribute this overestimation to data discontinuity in the aerosol height climatology derived from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite. OMI uses predefined aerosol heights over regions where CALIPSO climatology is not present, leading to the overestimation of SSA. The importance of aerosol height was also studied using the Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model. The results from the joint retrievals were validated using cruise-based measurements. It was seen that OMIMODIS SSA retrievals performed better than the OMI only retrieval over the Bay of Bengal during winter, when the aerosols are present closer to the surface. Discrepancy between satellite retrievals and cruise measurements was seen when elevated aerosols were present which might not have been detected by the cruise instruments.

Research areas: Validation