nasa-gesdisc/es-publications-researchareas huggingface dataset:
Evaluating the hydrological cycle over land using the newly-corrected precipitation climatology from the Global Precipitation Climatology Centre (GPCC) (2017)

The 2015 release of the precipitation climatology from the Global Precipitation Climatology Centre (GPCC) for 19512000, based on climatological normals of about 75,100 rain gauges, allows for quantification of mean land surface precipitation as part of the global water cycle. In GPCCs 2011-release, a bulk climatological correction was applied to compensate for gauge undercatch. In this paper we derive an improved correction approach based on the synoptic weather reports for the period 19822015. The compared results show that the climatological approach tends to overestimate the correction for Central and Eastern Europe, especially in the northern winter, and in other regions throughout the year. Applying the mean weather-dependent correction to the GPCCs uncorrected precipitation climatology for 19512000 gives a value of 854.7 mm of precipitation per year (excluding Antarctica) or 790 mm for the global land surface. The warming of nearly 1 K relative to pre-industrial temperatures is expected to be accompanied by a 2%3% increase in global (land and ocean) precipitation. However, a comparison of climatology for 30-year reference periods from 19311960 up to 19812010 reveals no significant trend for land surface precipitation. This may be caused by the large variability of precipitation, the varying data coverage over time and other issues related to the sampling of rain-gauge networks. The GPCC continues to enlarge and further improve the quality of its database, and will generate precipitation analyses with homogeneous data coverage over time. Another way to reduce the sampling issues is the combination of rain gauge-based analyses with remote sensing (i.e., satellite or radar) datasets.

Research areas: Validation
Satellite Remote Sensing for Water Resources Management: Potential for (2018)

Water resources management (WRM) for sustainable development presents many challenges in areas with sparse in situ monitoring networks. The exponential growth of satellite based information over the past decade provides unprecedented opportunities to support and improve WRM. Furthermore, traditional barriers to the access and usage of satellite data are lowering as technological innovations provide opportunities to manage and deliver this wealth of information to a wider audience. We review data needs for WRM and the role that satellite remote sensing can play to fill gaps and enhance WRM, focusing on the Latin American and Caribbean as an example of a region with potential to further develop its resources and mitigate the impacts of hydrological hazards. We review the state-of-the-art for relevant variables, current satellite missions, and products, how they are being used currently by national agencies across the Latin American and Caribbean region, and the challenges to improving their utility. We discuss the potential of recently launched, upcoming, and proposed missions that are likely to further enhance and transform assessment and monitoring of water resources. Ongoing challenges of accuracy, sampling, and continuity still need to be addressed, and further challenges related to the massive amounts of new data need to be overcome to best leverage the utility of satellite based information for improving WRM.

Research areas: Validation
Earth as a Proxy Exoplanet: Deconstructing and Reconstructing (2021)

Abstract
            Point-source spectrophotometric (single-point) light curves of Earth-like planets contain a surprising amount of information about the spatial features of those worlds. Spatially resolving these light curves is important for assessing time-varying surface features and the existence of an atmosphere, which in turn is critical to life on Earth and significant for determining habitability on exoplanets. Given that Earth is the only celestial body confirmed to harbor life, treating it as a proxy exoplanet by analyzing time-resolved spectral images provides a benchmark in the search for habitable exoplanets. The Earth Polychromatic Imaging Camera (EPIC) on the Deep Space Climate Observatory (DSCOVR) provides such an opportunity, with observations of 5000 full-disk sunlit Earth images each year at 10 wavelengths with high temporal frequency. We disk-integrate these spectral images to create single-point light curves and decompose them into principal components (PCs). Using machine-learning techniques to relate the PCs to six preselected spatial features, we find that the first and fourth PCs of the single-point light curves, contributing 83.23% of the light-curve variability, contain information about low and high clouds, respectively. Surface information relevant to the contrast between land and ocean reflectance is contained in the second PC, while individual land subtypes are not easily distinguishable (<0.1% total light-curve variation). We build an Earth model by systematically altering the spatial features to derive causal relationships to the PCs. This model can serve as a baseline for analyzing Earth-like exoplanets and guide wavelength selection and sampling strategies for future observations.

Research areas: Validation, Sun-Earth Interactions
A data-driven deep learning model for weekly sea ice concentration prediction of the Pan-Arctic during the melting season (2022)

This study proposes a purely data-driven model for the weekly prediction of daily sea ice concentration (SIC) of the pan-Arctic (90 N, 45 N, 180 E, 180 W) during the melting season. The model, SICNet, adopts an encoderdecoder framework with fully convolutional networks (FCNs) and can predict the SIC (covering  2$  in the SIE error.

Research areas: Validation, Cryospheric Indicators
Array DBMS in environmental science: Satellite sea surface height data in the Cloud (2017)

Nowadays environmental science experiences tremendous growth of raster data: N-dimensional (N-d) arrays coming mainly from numeric simulation and Earth remote sensing. An array DBMS is a tool to streamline raster data processing. However, raster data are usually stored in files, not in databases. Moreover, numerous command line tools exist for processing raster files. This paper describes a distributed array DBMS under development that partially delegates raster data processing to such tools. Our DBMS offers a new N-d array data model to abstract from the files and the tools and processes data in a distributed fashion directly in their native file formats. As a case study, popular satellite altimetry data were used for the experiments carried out on 8- and 16-nodes clusters in Microsoft Azure Cloud. New array DBMS is up to 70 faster than SciDB which is the only freely available distributed array DBMS to date.

Research areas: Validation
Development of mapping techniques for off road trafficability to support military operation (2020)

Terrain plays a vital role in any military operation, however terrain trafficability assessment is the key requirement for planning any military operation in advance. In this study, terrain conditions for off-road trafficability assessment, an integrated approach used because it is affected by large number of factors. While evaluating terrain mobility, its topography along with land use/land cover are important factors. Maps have been the basic ingredient of topographic information throughout history of planning. In the current context of work, to generate mobility maps, an out-of-box methodology was experimented. This was performed by evaluating the relative direct and in-direct parameters from satellite data and terrain ancillary information. Hence the main objective of this study was to assess the potential of all the factors in providing relevant assisting information for vehicle mobility. To assess the relative importance of factors and its contribution in estimating off road trafficability requires the domain expertise and field experience. Based on qualitative assessment along with field studies, the overall impact can be converted into quantitative terms and ultimately it will pave the way for automation of trafficability, which will be useful in planning of corridors during military operations.

Research areas: Validation
Constraining Reanalysis Snowfall Over the Arctic Ocean Using CloudSat Observations (2020)

In the absence of widespread snowfall observations over the Arctic Ocean, reanalysis products provide a wide range of estimates of time-evolving snowfall rates over Arctic sea ice, and it can be difficult to determine which product is most representative. In this work, Arctic snowfall rates retrieved from 2006 to 2016 CloudSat observations and snowfall products from three reanalyses are assessed. The products can be brought into encouraging agreement over the region on interannual time scales once differences in spatial representativeness and temporal sampling are accounted for. This motivates the use of CloudSat's snowfall product to calibrate reanalysis snowfall. The calibration is carried out for four Arctic quadrants and combined to produce regionally resolved and consistent estimates of interannually varying snowfall. Calibrated reanalysis snowfall inputs are then used to drive the NASA Eulerian Snow On Sea Ice Model, reducing the interproduct spread in the resulting simulated snow depths across the Arctic.

Research areas: Validation
The Atmospheric Infrared Sounder Obs4MIPs Version 2 Data Set (2019)

The Atmospheric Infrared Sounder (AIRS) Obs4MIPs (Observations for Model Intercomparison Projects) Version 2 data set includes monthly mean tropospheric air temperature, specific humidity, and relative humidity for each calendar month from September 2002 to September 2016, on a global 1° × 1° latitude-longitude spatial grid and on the Coupled Model Intercomparison Project eight mandatory vertical pressure levels from 1,000 to 300 hPa. It also includes standard error and number of observations, for an estimate of AIRS data retrieval error and sampling uncertainty. Three new technical notes describe the data set. This data set is designed for Coupled Model Intercomparison Project climate model evaluation and has been published at Earth System Grid Federation data centers since April 2018. It adds new monthly mean tropospheric relative humidity data to Obs4MIPs and updates and extends the monthly mean tropospheric air temperature and specific humidity data in AIRS Obs4MIPs Version 1 data set. The data source for this data set is the AIRS Version 6 Level 3 standard monthly mean air temperature, specific humidity, and relative humidity data products in the "TqJoint" grids from the AIRS and Advanced Microwave Sounding Unit A combined physical retrievals. This paper documents this data set in terms of motivation for this data set, data description, data origin and processing procedures, major improvements from the previous version, and caveats for its use in climate model evaluation.

Research areas: Validation
Intercomparison of airborne and surface-based measurements during the CLARIFY, ORACLES and LASIC field experiments (2022)

Abstract. Data are presented from intercomparisons between two
research aircraft, the FAAM BAe-146 and the NASA Lockheed P3, and between
the BAe-146 and the surface-based DOE (Department of Energy) ARM
(Atmospheric Radiation Measurement) Mobile Facility at Ascension Island
(8 S, 14.5 W; a remote island in the mid-Atlantic).
These took place from 17 August to 5 September 2017, during the African
biomass burning (BB) season. The primary motivation was to give confidence in the
use of data from multiple platforms with which to evaluate numerical climate
models. The three platforms were involved in the CLoudsAerosolRadiation
Interaction and Forcing for Year 2017 (CLARIFY-2017), ObseRvations of
Aerosols above CLouds and their intEractionS (ORACLES), and Layered Atlantic
Smoke and Interactions with Clouds (LASIC) field experiments. Comparisons
from flight segments on 6 d where the BAe-146 flew alongside the ARM
facility on Ascension Island are presented, along with comparisons from the
wing-tip-to-wing-tip flight of the P3 and BAe-146 on 18 August 2017.
The intercomparison flight sampled a relatively clean atmosphere overlying a
moderately polluted boundary layer, while the six fly-bys of the ARM site
sampled both clean and polluted conditions 24 km upwind. We compare and
validate characterisations of aerosol physical, chemical and optical
properties as well as atmospheric radiation and cloud microphysics between platforms.
We assess the performance of measurement instrumentation in the field, under
conditions where sampling conditions are not as tightly controlled as in
laboratory measurements where calibrations are performed. Solar radiation
measurements compared well enough to permit radiative closure studies.
Optical absorption coefficient measurements from all three platforms were
within uncertainty limits, although absolute magnitudes were too low
(<10 Mm1) to fully support a comparison of the absorption
Angstrom exponents. Aerosol optical absorption measurements from
airborne platforms were more comparable than aircraft-to-ground
observations. Scattering coefficient observations compared adequately
between airborne platforms, but agreement with ground-based measurements was
worse, potentially caused by small differences in sampling conditions or
actual aerosol population differences over land. Chemical composition
measurements followed a similar pattern, with better comparisons between the
airborne platforms. Thermodynamics, aerosol and cloud microphysical
properties generally agreed given uncertainties.

Research areas: Validation
Localizing the Water-Energy Nexus: The Relationship between Solar (2021)

Water availability plays an important role in the expansion planning of utility-scale solar power plants, especially in the arid regions of the Middle East and North Africa. Although these power plants usually account for only a small fraction of local water demand, competition for water resources between communities, farmers, companies, and power suppliers is already emerging and is likely to intensify in future. Despite this, to date there has been a lack of comprehensive studies analyzing interdependencies and potential conflicts between energy and water at local level. This study addresses this research gap and examines the linkages between water resources and energy technologies at local level based on a case study conducted in Ouarzazate, Morocco, where one of the largest solar power complexes in the world was recently completed. To better understand the challenges faced by the region in light of increased water demand and diminishing water supply, a mixed-method research design was applied to integrate the knowledge of local stakeholders through a series of workshops. In a first step, regional socio-economic water demand scenarios were developed and, in a second step, water saving measures to avoid critical development pathways were systematically evaluated using a participatory multi-criteria evaluation approach. The results are a set of water demand scenarios for the region and a preferential ranking of water saving measures that could be drawn upon to support decision-making relating to energy and water development in the region.

Research areas: Energy Production/Use
Atmospheric CO and CH4 time series and seasonal variations on Reunion Island from ground-based in situ and FTIR (NDACC and TCCON) measurements (2018)

Abstract. Atmospheric carbon monoxide (CO) and methane (CH4) mole fractions are measured by ground-based in situ cavity ring-down spectroscopy (CRDS) analyzers and Fourier transform infrared (FTIR) spectrometers at two sites (St Denis and Maido) on Reunion Island (21 S, 55 E) in the Indian Ocean. Currently, the FTIR Bruker IFS 125HR at St Denis records the direct solar spectra in the near-infrared range, contributing to the Total Carbon Column Observing Network (TCCON). The FTIR Bruker IFS 125HR at Maido records the direct solar spectra in the mid-infrared (MIR) range, contributing to the Network for the Detection of Atmospheric Composition Change (NDACC). In order to understand the atmospheric CO and CH4 variability on Reunion Island, the time series and seasonal cycles of CO and CH4 from in situ and FTIR (NDACC and TCCON) measurements are analyzed. Meanwhile, the difference between the in situ and FTIR measurements are discussed. The CO seasonal cycles observed from the in situ measurements at Maido and FTIR retrievals at both St Denis and Maido are in good agreement with a peak in SeptemberNovember, primarily driven by the emissions from biomass burning in Africa and South America. The dry-air column averaged mole fraction of CO (XCO) derived from the FTIR MIR spectra (NDACC) is about 15.7 ppb larger than the CO mole fraction near the surface at Maido, because the air in the lower troposphere mainly comes from the Indian Ocean while the air in the middle and upper troposphere mainly comes from Africa and South America. The trend for CO on Reunion Island is unclear during the 20112017 period, and more data need to be collected to get a robust result. A very good agreement is observed in the tropospheric and stratospheric CH4 seasonal cycles between FTIR (NDACC and TCCON) measurements, and in situ and the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) satellite measurements, respectively. In the troposphere, the CH4 mole fraction is high in AugustSeptember and low in DecemberJanuary, which is due to the OH seasonal variation. In the stratosphere, the CH4 mole fraction has its maximum in MarchApril and its minimum in AugustOctober, which is dominated by vertical transport. In addition, the different CH4 mole fractions between the in situ, NDACC and TCCON CH4 measurements in the troposphere are discussed, and all measurements are in good agreement with the GEOS-Chem model simulation. The trend of XCH4 is 7.60.4 ppb yr1 from the TCCON measurements over the 2011 to 2017 time period, which is consistent with the CH4 trend of 7.40.5 ppb yr1 from the in situ measurements for the same time period at St Denis.

Research areas: Validation
A high-resolution gridded dataset to assess electrification in sub-Saharan Africa (2019)

Spatially explicit data on electricity access and use are essential for effective policy-making and infrastructure planning in low-income, data-scarce regions. We present and validate a 1-km resolution electricity access dataset covering sub-Saharan Africa built on gridded nighttime light, population, and land cover data. Using light radiance probability distributions, we define electricity consumption tiers for urban and rural areas and estimate the by-tier split of consumers living in electrified areas. The approach provides new insight into the spatial distribution and temporal evolution of electricity access, and a measure of its quality beyond binary access. We find our estimates to be broadly consistent with recently published province- and national-level statistics. Moreover, we demonstrate consistency between the estimated electricity access quality indicators and survey-based consumption levels defined in accordance with the World Bank Multi-Tier Framework. The dataset is readily reproduced and updated using an open-access scientific computing framework. The data and approach can be applied for improving the assessment of least-cost electrification options, and examining links between electricity access and other sustainable development objectives.

Research areas: Validation
Trends in sunshine duration in Poland (1971–2018) (2021)

AbstractThis study aims to characterize temporal and spatial trends of sunshine duration in Poland based on data from the years 19712018 and to make an attempt to explain their causes. Daily sunshine duration came from 31 Polish meteorological stations. Data from 10 stations came only from a traditional CampbellStokes heliograph, while in the other 21 synoptic stations CampbellStokes heliograph was replaced with an automatic sensor in 2014 and since then only such data are available. The smallest sunshine duration occurs in December (average areal total39.7 hr), and the highest in July (average areal total230.1 hr). The spatial distribution of sunshine duration in Poland shows great diversity associated with different day lengths depending on latitude. In the cool part of the year (November, December, January, and February) the isolines of totals of sunshine duration run from the highest values in the south to the lowest in the north of the country, while in the months of the warm part of the year (March to September) the system is reversed. The multiannual trends in sunshine duration in Poland are similar to the changes taking place in other parts of the world. The years 19711980 mark the end of global dimming, which was manifested in very low values of actual and relative sunshine duration. Global brightening confirms a statistically significant growing trend in sunshine duration at all the analysed meteorological stations in Poland from April to September. There is a strong statistically significant relationship between the areal totals of sunshine duration and the type of pressure system, average monthly cloud cover, and atmospheric optical depth.

Research areas: Sun-Earth Interactions
Comprehensive evaluation method of micro-energy harvesters for power supply of outdoor wireless sensors (2021)

Abstract
            There have been many solar or wind micro-energy collection technologies for outdoor wireless sensors, but it is difficult to compare them horizontally and choose the appropriate scheme. To overcome this problem, this paper proposes a comprehensive evaluation method of micro-energy harvesters which includes an evaluation matrix composed of three types of indexes and the corresponding test method. After the evaluation matrix is obtained by testing, a CRITIC-based weight calculation method is used to get a comprehensive score. Finally, ten commonly used solar or wind micro-energy harvesters in five typical regions are compared horizontally by the proposed method. The results show that the evaluation method can comprehensively consider various factors, and harvesters with higher comprehensive scores tend to have more balanced performance in all aspects. Furthermore, in regions with rich wind resources, the wind and solar harvesters have complementary time-domain characteristics.

Research areas: Sun-Earth Interactions
An Improved ANN Model for Prediction of Solar Radiation Using Machine Learning Approach (2021)

An accurate forecast of weather is essential for obtaining energy from Renewable sources. The objective of this paper is to present an analysis of weather parameters and comparison among different models of the weather prediction from accessible parameters and finally deriving a new technique for solar prediction in support of photovoltaic output power. Artificial Neural Network model with 5 weather parameters from NASA POWER dataset have been utilized to predict the day-ahead solar radiation and evaluated against real data measured for 4 years at Agartala, India (Latitude 23.83 N and Longitude 91.282 E). Results detailed in this work confirm the best predicting potential of the proposed method. The proposed model has been shown to predict solar radiation with accuracy of 83% shows the robustness of the system.

Research areas: Sun-Earth Interactions