nasa-gesdisc/es-publications-researchareas huggingface dataset:
Estimating gravity field and quasi-geoid in Cameroon (CGM20) (2021)

In this paper, the computation of the new gravimetric quasi-geoid of Cameroon based on some recent global geopotential models and a detailed Digital Terrain Model (DTM) of the area is presented. Besides gravity data supplied by the Bureau Gravimetrique International (BGI) new data have been collected by the National Institute of Cartography (NIC) of Cameroon and used in the computation. Three different quasi-geoid models have been obtained based on three different global models, namely GOCE-dir5, EGM2008 and XGM2019e_2159. The well-known remove-compute-restore technique has been applied and Fast Collocation has been used for estimating the residual quasi-geoid component from gridded gravity data. The comparisons with Global Navigation Satellite System/levelling (GNSS/levelling) data distributed over the Cameroon territory show that some improvements have been obtained either with respect to the global model solutions and to previous local estimates computed in the past.

Research areas: Validation
Remotely sensed ensembles of the terrestrial water budget over major global river basins: An assessment of three closure techniques (2021)

Remote sensing is a useful tool for observing the water cycle. However, combining remote sensing products over any major river basin will result in a residual error in the overall water balance. Previous studies have either quantified this error without correcting it, or have merged observations together with land surface models (LSMs) to produce a single best estimate of the water balance. Here, we present a new approach in which combinations of remote sensing and in situ observations are constrained to enforce water balance closure. Rather than a single estimate, this produces an ensemble of unique water balance estimates intended to characterize uncertainty and to avoid biases implicit in LSMs. We evaluate three techniques of varying complexity to enforce water balance closure for individual ensemble members over 24 global basins from Oct. 2002 - Dec. 2014, resulting in as many as 60 realizations of the monthly water budget, contingent upon data availability. Compared with a published climate data record, the ensemble shows strong agreement for precipitation, evapotranspiration and changes in storage (R2: 0.910.95), with less agreement for streamflow (R2: 0.420.47), which may be indicative of LSM biases in the climate data record. Water balance residual errors resulting from combinations of raw products vary significantly (p 

Research areas: Land Surface/Agriculture Indicators, Validation
Fifty Years of Research on the MaddenJulian Oscillation: Recent Progress, Challenges, and Perspectives (2020)

Since its discovery in the early 1970s, the crucial role of the Madden-Julian Oscillation (MJO) in the global hydrological cycle and its tremendous influence on high-impact climate and weather extremes have been well recognized. The MJO also serves as a primary source of predictability for global Earth system variability on subseasonal time scales. The MJO remains poorly represented in our state-of-the-art climate and weather forecasting models, however. Moreover, despite the advances made in recent decades, theories for the MJO still disagree at a fundamental level. The problems of understanding and modeling the MJO have attracted significant interest from the research community. As a part of the AGU's Centennial collection, this article provides a review of recent progress, particularly over the last decade, in observational, modeling, and theoretical study of the MJO. A brief outlook for near-future MJO research directions is also provided.

Research areas: Atmospheric/Ocean Indicators
Statistical and visual quality assessment of nearly-global and continental digital elevation models of Trentino, Italy (2019)

Nearly-global digital elevation models (DEMs)  Shuttle Radar Topography Mission DEM (SRTM1 DEM), Advanced Spaceborne Thermal Emission and Reflection Radiometer Global DEM (ASTER GDEM), and Advanced Land Observing Satellite World 3D digital surface model (AW3D30 DSM)  are widely used in geosciences. A nearly-continental European DEM (EU-DEM) is also publicly available. We assess statistical accuracy and artefact occurrence of the latest versions of these models for Trentino, Italian Alps. We used a set of 111 geodetic network points and a lidar-based reference DEM. Statistically, AW3D30 DSM has the highest accuracy, while EU-DEM is marked by the worst characteristics. To visualize the spatial distribution of errors, we calculated residuals between the reference and validated DEMs. Voids abound in SRTM1 DEM that makes problematic its application in the Alpine region. Randomly distributed, grain-like artefacts are common for ASTER GDEM. AW3D30 DSM includes artefacts caused by to the Gibbs phenomenon. Retaining defects of SRTM1 DEM and ASTER GDEM, EU-DEM has artefacts caused by its hydrological correction. Usual statistical evaluation of DEM accuracy is not capable to describe completely DEM quality. A robust spatial analysis, including interactive visual inspection, should be adopted for a quality control of DEMs.

Research areas: Validation
Reanalysis comparisons of upper troposphericlower stratospheric jets and multiple tropopauses (2017)

Abstract. The representation of upper troposphericlower stratospheric (UTLS) jet and tropopause characteristics is compared in five modern high-resolution reanalyses for 1980 through 2014. Climatologies of upper tropospheric jet, subvortex jet (the lowermost part of the stratospheric vortex), and multiple tropopause frequency distributions in MERRA (Modern-Era Retrospective analysis for Research and Applications), ERA-I (ERA-Interim; the European Centre for Medium-Range Weather Forecasts, ECMWF, interim reanalysis), JRA-55 (the Japanese 55-year Reanalysis), and CFSR (the Climate Forecast System Reanalysis) are compared with those in MERRA-2. Differences between alternate products from individual reanalysis systems are assessed; in particular, a comparison of CFSR data on model and pressure levels highlights the importance of vertical grid spacing. Most of the differences in distributions of UTLS jets and multiple tropopauses are consistent with the differences in assimilation model grids and resolution  for example, ERA-I (with coarsest native horizontal resolution) typically shows a significant low bias in upper tropospheric jets with respect to MERRA-2, and JRA-55 (the Japanese 55-year Reanalysis) a more modest one, while CFSR (with finest native horizontal resolution) shows a high bias with respect to MERRA-2 in both upper tropospheric jets and multiple tropopauses. Vertical temperature structure and grid spacing are especially important for multiple tropopause characterizations. Substantial differences between MERRA and MERRA-2 are seen in mid- to high-latitude Southern Hemisphere (SH) winter upper tropospheric jets and multiple tropopauses as well as in the upper tropospheric jets associated with tropical circulations during the solstice seasons; some of the largest differences from the other reanalyses are seen in the same times and places. Very good qualitative agreement among the reanalyses is seen between the large-scale climatological features in UTLS jet and multiple tropopause distributions. Quantitative differences may, however, have important consequences for transport and variability studies. Our results highlight the importance of considering reanalyses differences in UTLS studies, especially in relation to resolution and model grids; this is particularly critical when using high-resolution reanalyses as an observational reference for evaluating global chemistryclimate models.

Research areas: Validation
Enhancing interoperability and capabilities of earth science data using the Observations Data Model 2 (ODM2) (2017)

Earth Science researchers require access to integrated, cross-disciplinary data in order to answer critical research questions. Partially due to these science drivers, it is common for disciplinary data systems to expand from their original scope in order to accommodate collaborative research. The result is multiple disparate databases with overlapping but incompatible data. In order to enable more complete data integration and analysis, the Observations Data Model Version 2 (ODM2) was developed to be a general information model, with one of its major goals to integrate data collected by in situ sensors with those by ex-situ analyses of field specimens. Four use cases with different science drivers and disciplines have adopted ODM2 because of benefits to their users. The disciplines behind the four cases are diverse  hydrology, rock geochemistry, soil geochemistry, and biogeochemistry. For each case, we outline the benefits, challenges, and rationale for adopting ODM2. In each case, the decision to implement ODM2 was made to increase interoperability and expand data and metadata capabilities. One of the common benefits was the ability to use the flexible handling and comprehensive description of specimens and data collection sites in ODM2s sampling feature concept. We also summarize best practices for implementing ODM2 based on the experience of these initial adopters. The descriptions here should help other potential adopters of ODM2 implement their own instances or to modify ODM2 to suit their needs.

Research areas: Validation
An improved near-surface specific humidity and air temperature climatology for the SSM/I satellite period (2015)

Abstract A near-surface specific humidity (Qa) and air temperature (Ta) climatology on daily and 0.25 grids was constructed by the objectively analyzed airsea fluxes (OAFlux) project by objectively merging two recent satellite-derived high-resolution analyses, the OAFlux existing 1 analysis, and atmospheric reanalyses. The two satellite products include the multi-instrument microwave regression (MIMR) Qa and Ta analysis and the Goddard Satellite-Based Surface Turbulent Fluxes, version 3 (GSSTF3), Qa analysis. This study assesses the degree of improvement made by OAFlux using buoy time series measurements at 137 locations and a global empirical orthogonal function (EOF) analysis. There are a total of 130 855 collocated daily values for Qa and 283 012 collocated daily values for Ta in the buoy evaluation. It is found that OAFlux Qa has a mean difference close to 0 and a root-mean-square (RMS) difference of 0.73 g kg1, and Ta has a mean difference of 0.03C and an RMS difference of 0.45C. OAFlux shows no major systematic bias with respect to buoy measurements over all buoy locations except for the vicinity of the Gulf Stream boundary current, where the RMS difference exceeds 1.8C in Ta and 1.2 g kg1 in Qa. The buoy evaluation indicates that OAFlux represents an improvement over MIMR and GSSTF3. The global EOF-based intercomparison analysis indicates that OAFlux has a similar spatialtemporal variability pattern with that of three atmospheric reanalyses including MERRA, NCEP-1, and ERA-Interim, but that it differs from GSSTF3 and the Climate Forecast System Reanalysis (CFSR).

Research areas: Validation
A Fundamental Climate Data Record of SMMR, SSM/I, and SSMIS brightness temperatures (2020)

Abstract. The Fundamental Climate Data Record (FCDR) of Microwave Imager Radiances from the Satellite Application Facility on Climate Monitoring (CM SAF)
comprises inter-calibrated and homogenized brightness temperatures from the Scanning Multichannel Microwave Radiometer (SMMR), the Special Sensor
Microwave/Imager (SSM/I), and the Special Sensor Microwave Imager/Sounder SSMIS radiometers. It covers the time period from October 1978 to
December 2015 including all available data from the SMMR radiometer aboard Nimbus-7 and all SSM/I and SSMIS radiometers aboard the Defense
Meteorological Satellite Program (DMSP) platforms. SMMR, SSM/I, and SSMIS data are used for a variety of applications, such as analyses of the
hydrological cycle, remote sensing of sea ice, or as input into reanalysis projects. The improved homogenization and inter-calibration procedure
ensures the long-term stability of the FCDR for climate-related applications. All available raw data records from different sources have been
reprocessed to a common standard, starting with the calibration of the raw Earth counts, to ensure a completely homogenized data record. The data
processing accounts for several known issues with the instruments and corrects calibration anomalies due to along-scan inhomogeneity, moonlight
intrusions, sunlight intrusions, and emissive reflector. Corrections for SMMR are limited because the SMMR raw data records were not available.
Furthermore, the inter-calibration model incorporates a scene dependent inter-satellite bias correction and a non-linearity correction in the
instrument calibration. The data files contain all available original sensor data (SMMR: Pathfinder level 1b) and metadata to provide a completely
traceable climate data record. Inter-calibration and Earth incidence angle normalization offsets are available as additional layers within the data
files in order to keep this information transparent to the users. The data record is complemented with noise-equivalent temperatures (NeT),
quality flags, surface types, and Earth incidence angles. The FCDR together with its full documentation, including evaluation results, is freely
available at: https://doi.org/10.5676/EUM_SAF_CM/FCDR_MWI/V003 (Fennig et al., 2017).

Research areas: Validation
Quality assessment and improvement of the EUMETSAT Meteosat surface albedo climate data record (2015)

Abstract. Surface albedo has been identified as an important parameter for understanding and quantifying the Earth's radiation budget. EUMETSAT generated the Meteosat Surface Albedo (MSA) Climate Data Record (CDR) currently comprising up to 24 years (19822006) of continuous surface albedo coverage for large areas of the Earth. This CDR has been created within the Sustained, Coordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE-CM) framework. The long-term consistency of the MSA CDR is high and meets the Global Climate Observing System (GCOS) stability requirements for desert reference sites. The limitation in quality due to non-removed clouds by the embedded cloud screening procedure is the most relevant weakness in the retrieval process. A twofold strategy is applied to efficiently improve the cloud detection and removal. The first step consists of the application of a robust and reliable cloud mask, taking advantage of the information contained in the measurements of the infrared and visible bands. Due to the limited information available from old radiometers, some clouds can still remain undetected. A second step relies on a post-processing analysis of the albedo seasonal variation together with the usage of a background albedo map in order to detect and screen out such outliers. The usage of a reliable cloud mask has a double effect. It enhances the number of high-quality retrievals for tropical forest areas sensed under low view angles and removes the most frequently unrealistic retrievals on similar surfaces sensed under high view angles. As expected, the usage of a cloud mask has a negligible impact on desert areas where clear conditions dominate. The exploitation of the albedo seasonal variation for cloud removal has good potentialities but it needs to be carefully addressed. Nevertheless it is shown that the inclusion of cloud masking and removal strategy is a key point for the generation of the next MSA CDR release.

Research areas: Validation
Variational Retrievals of High Winds Using Uncalibrated CyGNSS (2020)

This study presents a new retrieval approach for obtaining wind speeds from CyGNSS level-1 observables. Unlike other existing approaches, (1) this one is a variational technique that is based on a physical forward model, (2) it uses uncalibrated bin raw counts observables, (3) the geophysical information content comes from only one pixel of the broader delay-Doppler map, finest achievable resolution in level-1 products over the sea, and (4) calibrates them against track-wise polynomial adjustments to a background numerical weather prediction model. Through comparisons with the background model, other spaceborne sensors (SMAP, SMOS, ASCAT-A/B), and CyGNSS wind retrievals by other organizations, the study shows that this approach has skills to infer wind speeds, including hurricane force winds. For example, the Pearsons correlation coefficient between these CyGNSS retrievals and ERA5 is 0.884, 0.832 with NOAA CyGNSS results, and 0.831 with respect to SMAP co-located measurements. Furthermore, the variational retrieval algorithm is a simplified version of the more general equations that are used in data assimilation, and the calibration scheme could also be integrated in the assimilation process. Therefore, this approach is also a good tool for analyzing the potential performance of ingesting uncalibrated level-1 single-pixel observables into NWP.

Research areas: Validation
Skill improvement of dynamical seasonal Arctic sea ice forecasts (2016)

We explore the error and improve the skill of the outcome from dynamical seasonal Arctic sea ice reforecasts using different bias correction and ensemble calibration methods. These reforecasts consist of a five-member ensemble from 1979 to 2012 using the general circulation model EC-Earth. The raw model reforecasts show large biases in Arctic sea ice area, mainly due to a differently simulated seasonal cycle and long term trend compared to observations. This translates very quickly (1-3 months) into large biases. We find that (heteroscedastic) extended logistic regressions are viable ensemble calibration methods, as the forecast skill is improved compared to standard bias correction methods. Analysis of regional skill of Arctic sea ice shows that the Northeast Passage and the Kara and Barents Sea are most predictable. These results show the importance of reducing model error and the potential for ensemble calibration in improving skill of seasonal forecasts of Arctic sea ice.

Research areas: Validation
Ancillary Data Uncertainties within the SeaDAS Uncertainty Budget for Ocean Colour Retrievals (2022)

Atmospheric corrections introduce uncertainties in bottom-of-atmosphere Ocean Colour (OC) products. In this paper, we analyse the uncertainty budget of the SeaDAS atmospheric correction algorithm. A metrological approach is followed, where each of the error sources are identified in an uncertainty tree diagram and briefly discussed. Atmospheric correction algorithms depend on ancillary variables (such as meteorological properties and column densities of gases), yet the uncertainties in these variables were not studied previously in detail. To analyse these uncertainties for the first time, the spread in the ERA5 ensemble is used as an estimate for the uncertainty in the ancillary data, which is then propagated to uncertainties in remote sensing reflectances using a Monte Carlo approach and the SeaDAS atmospheric correction algorithm. In an example data set, wind speed and relative humidity are found to be the main contributors (among the ancillary parameters) to the remote sensing reflectance uncertainties.

Research areas: Validation
On the influence of underlying elevation data on Sentinel-5 Precursor TROPOMI satellite methane retrievals over Greenland (2022)

Abstract. The Sentinel-5 Precursor (S5P) mission was launched on October 2017 and has since provided data with high spatio-temporal resolution using its remote sensing instrument, the TROPOspheric Monitoring Instrument (TROPOMI). The latter is a nadir viewing passive grating imaging spectrometer. The mathematical inversion of the TROPOMI data yields retrievals of different trace gas and aerosol data products. The column-averaged dry-air mole fraction of methane (XCH4) is the product of interest to this study. The daily global coverage of the atmospheric methane mole fraction data enables the analysis of the methane distribution and variation on large scales and also to estimate surface emissions. The spatio-temporal high-resolution satellite data are potentially particularly valuable in remote regions, such as the Arctic, where few ground stations and in situ measurements are available. In addition to the operational Copernicus S5P total-column-averaged dry-air mole fraction methane data product developed by SRON, the scientific TROPOMI/WFMD algorithm data product v1.5 (WFMD product) was generated at the Institute of Environmental Physics at the University of Bremen. In this study we focus on the assessment of both S5P XCH4 data products over Greenland and find that spatial maps of both products show distinct features along the coastlines. Anomalies up to and exceeding 100 ppb are observed and stand out in comparison to the otherwise smooth changes in the methane distribution. These features are more pronounced for the operational product compared to the WFMD product. The spatial patterns correlate with the difference between the GMTED2010 digital elevation model (DEM) used in the retrievals and a more recent topography dataset, indicating that inaccuracies in the assumed surface elevation are the origin of the observed features. These correlations are stronger for the WFMD product. In order to evaluate the impact of the topography dataset on the retrieval we reprocess the WFMD product with updated elevation data. We find that a significant reduction of the localized features when GMTED2010 is replaced by recent topography data over Greenland based on ICESat-2 data. This study shows the importance of the chosen topography data for retrieved dry-air mole fractions. The use of a DEM that is as accurate and as up to date as possible is advised for all S5P data products as well as for future missions which rely on a DEM as input data. A modification based on this study is planned to be introduced in the next version of the WFMD data product.

Research areas: Greenhouse Gases, Validation
Evaluation of OCO2 X Variability at Local and Synoptic Scales using Lidar and In Situ Observations from the ACTAmerica Campaigns (2020)

With nearly 1 million observations of column-mean carbon dioxide concentration (X<SUB>CO<SUB>2</SUB></SUB>) per day, the Orbiting Carbon Observatory 2 (OCO-2) presents exciting possibilities for monitoring the global carbon cycle, including the detection of subcontinental column CO<SUB>2</SUB> variations. While the OCO-2 data set has been shown to achieve target precision and accuracy on a single-sounding level, the validation of X<SUB>CO<SUB>2</SUB></SUB> spatial gradients on subcontinental scales remains challenging. In this work, we investigate the use of an integrated path differential absorption (IPDA) lidar for evaluation of OCO-2 observations via NASA's Atmospheric Carbon and Transport (ACT)-America project. The project has completed eight clear-sky underflights of OCO-2 with the Multifunctional Fiber Laser Lidar (MFLL)—along with a suite of in situ instruments—giving a precisely colocated, high-resolution validation data set spanning nearly 3,800 km across four seasons. We explore the challenges and opportunities involved in comparing the MFLL and OCO-2 X<SUB>CO<SUB>2</SUB></SUB> data sets and evaluate their agreement on synoptic and local scales. We find that OCO-2 synoptic-scale gradients generally agree with those derived from the lidar, typically to ±0.1 ppm per degree latitude for gradients ranging in strength from 0 to 1 ppm per degree latitude. CO<SUB>2</SUB> reanalysis products also typically agree to ±0.25 ppm per degree when compared with an in situ-informed CO<SUB>2</SUB> "curtain." Real X<SUB>CO<SUB>2</SUB></SUB> features at local scales, however, remain challenging to observe and validate from space, with correlation coefficients typically below 0.35 between OCO-2 and the MFLL. Even so, ACT-America data have helped investigate interesting local X<SUB>CO<SUB>2</SUB></SUB> patterns and identify systematic spurious cloud-related features in the OCO-2 data set.

Research areas: Greenhouse Gases, Validation