nasa-gesdisc/es-publications-researchareas huggingface dataset:
Differential absorption lidar measurements of water vapor by the High Altitude Lidar Observatory (HALO): retrieval framework and first results (2022)

Abstract. Airborne differential absorption lidar (DIAL) offers a uniquely capable solution to the problem of measuring water vapor (WV) with high precision, accuracy, and resolution throughout the troposphere and lower stratosphere. The High Altitude Lidar Observatory (HALO) airborne WV DIAL was recently developed at NASA Langley Research Center and was first deployed in 2019. It uses four wavelengths near 935 nm to achieve sensitivity over a wide dynamic range and simultaneously employs 1064 nm backscatter and 532 nm high-spectral-resolution lidar (HSRL) measurements for aerosol and cloud profiling. A key component of the WV retrieval framework is flexibly trading resolution for precision to achieve optimal datasets for scientific objectives across scales. An approach to retrieving WV in the lowest few hundred meters of the atmosphere using the strong surface return signal is also presented. The five maiden flights of the HALO WV DIAL spanned the tropics through midlatitudes with a wide range of atmospheric conditions, but opportunities for validation were sparse. Comparisons to dropsonde WV profiles were qualitatively in good agreement, though statistical analysis was impossible due to systematic error in the dropsonde measurements. Comparison of HALO to in situ WV measurements aboard the aircraft showed no substantial bias across 3 orders of magnitude, despite variance (R2=0.66) that may be largely attributed to spatiotemporal variability. Precipitable water vapor measurements from the spaceborne sounders AIRS and IASI compared very well to HALO with R2>0.96 over ocean.

Research areas: Validation
Digital elevation models: Terminology and definitions (2021)

Digital elevation models (DEMs) provide fundamental depictions of the three-dimensional shape of the Earths surface and are useful to a wide range of disciplines. Ideally, DEMs record the interface between the atmosphere and the lithosphere using a discrete two-dimensional grid, with complexities introduced by the intervening hydrosphere, cryosphere, biosphere, and anthroposphere. The treatment of DEM surfaces, affected by these intervening spheres, depends on their intended use, and the characteristics of the sensors that were used to create them. DEM is a general term, and more specific terms such as digital surface model (DSM) or digital terrain model (DTM) record the treatment of the intermediate surfaces. Several global DEMs generated with optical (visible and near-infrared) sensors and synthetic aperture radar (SAR), as well as single/multi-beam sonars and products of satellite altimetry, share the common characteristic of a georectified, gridded storage structure. Nevertheless, not all DEMs share the same vertical datum, not all use the same convention for the area on the ground represented by each pixel in the DEM, and some of them have variable data spacings depending on the latitude. This paper highlights the importance of knowing, understanding and reflecting on the sensor and DEM characteristics and consolidates terminology and definitions of key concepts to facilitate a common understanding among the growing community of DEM users, who do not necessarily share the same background.

Research areas: Validation
Scenario archetypes reveal risks and opportunities for global mountain futures (2021)

Mountain social-ecological systems (MtSES) are transforming rapidly due to changes in multiple environmental and socioeconomic drivers. However, the complexity and diversity of MtSES present challenges for local communities, researchers and decision makers seeking to anticipate change and promote action towards sustainable MtSES. Participatory scenario planning can reveal potential futures and their interacting dynamics, while archetype analysis aggregates insights from site-based scenarios. We combined a systematic review of the global MtSES participatory scenarios literature and archetype analysis to identify emergent MtSES archetypal configurations. An initial sample of 1983 rendered 42 articles that contained 142 scenarios within which were 852 futures states. From these future states within the scenarios, we identified 59 desirable and undesirable futures that were common across studies. These common futures were grouped into four clusters that correlated significantly with three social-ecological factors (GDP per capita, income inequality, and mean annual temperature). Using these clusters and their associated significant factors, we derived four MtSES scenario archetypal configurations characterized by similar key adaptation strategies, assumptions, risks, and uncertainties. We called these archetypes: (1) revitalization through effective institutions and tourism; (2) local innovations in smallholder farming and forestry; (3) upland depopulation and increased risk of hazards; and (4) regulated economic and ecological prosperity. Results indicate risks to be mitigated, including biodiversity loss, ecosystem degradation, cultural heritage change, loss of connection to the land, weak leadership, market collapse, upland depopulation, increased landslides, avalanches, mudflows and rock falls, as well as climate variability and change. Transformative opportunities lie in adaptive biodiversity conservation, income diversification, adaptation to market fluxes, improving transport and irrigation infrastructure, high quality tourism and preserving traditional knowledge. Despite the uncertainties arising from global environmental changes, these archetypes support better targeting of evidence-informed actions across scales and sectors in MtSES.

Research areas: Land Surface/Agriculture Indicators
Performance assessment of MOD16 in evapotranspiration evaluation in Northwestern Mexico (2018)

Evapotranspiration (ET) is the second largest component of the water cycle in arid and semiarid environments, and, in fact, more than 60% of the precipitation on earth is returned to the atmosphere through it. MOD16 represents an operational source of ET estimates with adequate spatial resolution for several applications, such as water resources planning, at a regional scale. However, the use of these estimates in routine applications will require MOD16 evaluation and validation using accurate ground-based measurements. The main objective of this study was to evaluate the performance of the MOD16A2 product by comparing it with eddy covariance (EC) systems. Additional objectives were the analysis of the limitations, uncertainties, and possible improvements of the MOD16-estimated ET. The EC measurements were acquired for five sites and for a variety of land covers in northwestern Mexico. The indicators used for the comparison were: root mean square error (RMSE), bias (BIAS), concordance index (d), and determination coefficient (R2) of the correlation, comparing measured and modelled ET. The best performance was observed in Rayon (RMSE = 0.77 mmday1, BIAS = 0.46 mmday1, d = 0.88, and R2 = 0.86); El Mogor and La Paz showed errors and coefficients of determination comparable to each other (RMSE = 0.39 mmday1, BIAS = 0.04 mmday1, R2 = 0.46 and RMSE = 0.42 mmday1, BIAS = 0.18 mmday1, R2 = 0.45, respectively). In most cases, MOD16 underestimated the ET values.

Research areas: Land Surface/Agriculture Indicators, Validation
Extension of satellite Altimetry Jason-2 sea level anomalies towards the red sea coast using polynomial harmonic techniques (2017)

Satellite altimetry data are facing big challenges near the coasts. These challenges arise due to the fundamental difficulties of correction and land contamination in the foot print, which result in rejection of these data near the coast. Several studies have been carried out to extend these data towards the coast. Over the Red Sea, altimetry data consist of gaps, which extend to about 3050 km from the coast. Two methods are used for processing and extending Jason-2 satellite altimetry sea level anomalies (SLAs) towards the Red Sea coast; Fourier Series Model (FSM), and the polynomial sum of sine model (SSM). FSM model technique uses Fourier series and statistical analysis reflects strong relationship with both the observation and AVISO data, with strong and positive correlation. The second prediction technique, SSM model, depends on the polynomial sum of sine, and does not reflect any relationship with the observations and AVISO data close to the coast and the correlation coefficient (CC) is weak and negative. The FSM model output results in SLA data significantly better and more accurate than the SSM model output.

Research areas: Validation
Lightweight and robust representation of economic scales from satellite imagery (2020)

Satellite imagery has long been an attractive data source providing a wealth of information regarding human-inhabited areas. While high-resolution satellite images are rapidly becoming available, limited studies have focused on how to extract meaningful information regarding human habitation patterns and economic scales from such data. We present READ, a new approach for obtaining essential spatial representation for any given district from high-resolution satellite imagery based on deep neural networks. Our method combines transfer learning and embedded statistics to efficiently learn the critical spatial characteristics of arbitrary size areas and represent such characteristics in a fixed-length vector with minimal information loss. Even with a small set of labels, READ can distinguish subtle differences between rural and urban areas and infer the degree of urbanization. An extensive evaluation demonstrates that the model outperforms state-of-the-art models in predicting economic scales, such as the population density in South Korea (R2=0.9617), and shows a high use potential in developing countries where district-level economic scales are unknown.

Research areas: Habitat Conversion/Fragmentation
A 30 m global map of elevation with forests and buildings removed (2022)

Abstract
            Elevation data are fundamental to many applications, especially in geosciences. The latest global elevation data contains forest and building artifacts that limit its usefulness for applications that require precise terrain heights, in particular flood simulation. Here, we use machine learning to remove buildings and forests from the Copernicus Digital Elevation Model to produce, for the first time, a global map of elevation with buildings and forests removed at 1 arc second (30 m) grid spacing. We train our correction algorithm on a unique set of reference elevation data from 12 countries, covering a wide range of climate zones and urban extents. Hence, this approach has much wider applicability compared to previous DEMs trained on data from a single country. Our method reduces mean absolute vertical error in built-up areas from 1.61 to 1.12 m, and in forests from 5.15 to 2.88 m. The new elevation map is more accurate than existing global elevation maps and will strengthen applications and models where high quality global terrain information is required.

Research areas: Validation
The Imperatives of Sustainable Development (2017)

AbstractThe United Nations sustainable development goals are under fire. By attempting to cover all that is good and desirable in society, these targets have ended up as vague, weak, or meaningless. We suggest a model for sustainable development based on three moral imperatives: satisfying human needs, ensuring social equity, and respecting environmental limits. The model reflects Our Common Future's central message, moral imperatives laid out in philosophical texts on needs and equity, and recent scientific insights on environmental limits. The model is in conflict with the popular threepillar model of sustainable development, which seeks to balance social, environmental, and economic targets. Rather, we argue that sustainable development constitutes a set of constraints on human behaviour, including constraints on economic activity. By identifying indicators, and thresholds, we illustrate that different regions or groups of countries face different challenges. Copyright 2016 John Wiley & Sons, Ltd and ERP Environment

Research areas: Habitat Conversion/Fragmentation
The first Met Office Unified ModelJULES Regional Atmosphere and Land configuration, RAL1 (2020)

Abstract. In this paper we define the first Regional Atmosphere and Land (RAL) science configuration for kilometre-scale modelling using the Unified Model (UM) as the basis for the atmosphere and the Joint UK Land Environment Simulator (JULES) for the land. RAL1 defines the science configuration of the dynamics and physics schemes of the atmosphere and land. This configuration will provide a model baseline for any future weather or climate model developments to be described against, and it is the intention that from this point forward significant changes to the system will be documented in the literature. This reproduces the process used for global configurations of the UM, which was first documented as a science configuration in 2011. While it is our goal to have a single defined configuration of the model that performs effectively in all regions, this has not yet been possible. Currently we define two sub-releases, one for mid-latitudes (RAL1-M) and one for tropical regions (RAL1-T). The differences between RAL1-M and RAL1-T are documented, and where appropriate we define how the model configuration relates to the corresponding configuration of the global forecasting model.

Research areas: Validation
Nighttimelights satellite imagery reveals hotspots of second home mobility in rural Russia (a case study of Yaroslavl Oblast) (2021)

AbstractSecond home mobility is a wellknown phenomenon in many countries, but is widely prominent in Russia, where millions of city dwellers move to rural areas during the summertime. Combating longterm economic decline and depopulation, second home mobility creates a promising opportunity to revitalize the countryside. While this phenomenon is largely neglected by official statistics, we suggest using satellite imagery of nighttime lights to investigate its spatial and temporal patterns. We did this with the example of Yaroslavl Oblast in Russia. This region neighbors the Moscow Capital Region. It experiences a significant inflow of second home residents. By tracking the seasonal pixelwise changes of nighttime light radiance in monthly composites of satellite imagery from 2015 to 2019, we located hotspots of second homes and factors determining their spatial spread in rural areas. The results were evaluated with field research. Our results confirmed earlier conclusions that second homes locations in rural areas are largely determined by their proximity to Moscow, natural conditions, and transport accessibility. City dwellers often choose small and even fully abandoned villages for their second homes, which stresses the important role of second home mobility in preserving cultural landscapes. The proposed data and methods are limited by missing data for the northern regions during summer months and are more suitable for areas beyond the urban fringe where nighttimelights data are not biased by the overglow of large cities.

Research areas: Sun-Earth Interactions, Heat, Habitat Conversion/Fragmentation
All-sky longwave downward radiation from satellite measurements: General parameterizations based on LST, column water vapor and cloud top temperature (2020)

Remotely sensed surface longwave downward radiation (LWDR) plays an essential role in studying the surface energy budget and greenhouse effect. Most existing satellite-based methods or products depend on variables that are not readily available from space such as, liquid water path, air temperature, vapor pressure and/or cloud-base temperature etc., which seriously restrict the wide applications of satellite data. In this paper, new nonlinear parameterizations and a machine learning-based model for deriving all-sky LWDR are proposed based only on land surface temperature (LST), column water vapor and cloud-top temperature (CTT), that are relatively readily available day and night for most satellite missions. It is the first time to incorporate the CTT in the parameterizations for estimating LWDR under the cloudy-sky conditions. The results reveal that the new models work well and can derive all-sky global LWDR with reasonable accuracies (RMSE <23 W/m2, bias <2.0 W/m2). The convenience of input data makes the new models easy to use, and thus will definitely expand the applicability of remotely sensed measurements in radiation budget fields and many land applications.

Research areas: Sun-Earth Interactions, Validation
Evaluation of evapotranspiration estimation under cloud impacts over (2022)

Evapotranspiration (ET) is an important component of the hydrological cycle and energy balance in a land-atmosphere system. Satellite remote sensing has been widely used to estimate regional and global ET, but most previous methods depend on optical measurements that are limited to cloud-free conditions. This makes ET estimation challenging under cloudy sky. Currently, evaluations of satellite ET estimation under various cloud conditions remain lacking at the regional scale. Owing to the ability to penetrate clouds, satellite passive microwave measurements are powerful tools for retrieving ET under clouds. This study evaluated a satellite microwave-based daily ET method under all sky conditions over the part of China between 18°N and 50°N from 2003 to 2010, using microwave emissivity difference vegetation index (EDVI) as the proxy of vegetation water content (VWC). Validations using the surface water balance method found that the estimated ET (EDVI-ET) had an overall small bias (6.18%) in eight river basins. EDVI-ET displayed consistent spatiotemporal patterns with global MOD16 ET, with high spatial correlation (R&gt;0.71) and monthly temporal correlation (R&gt;0.82) throughout four seasons. Their differences were also small (&lt;0.56mmday<SUP>-1</SUP>) in forests, savannas, grass/shrubs, and croplands. Furthermore, cloud impacts on the regional ET were found to be significant and spatiotemporally heterogeneous. Both EDVI-ET and in-situ observations at seven flux towers indicated that cloud-induced reduction in daily ET could exceed 30% when the cloud cover increased by 60% (R<SUP>2</SUP> of 0.42, fitting line slope of 0.80, p&lt;0.001). Under increased cloud conditions in summer, the changes in net radiation dominated the ET over dense vegetation in southern China, while the roles of air temperature and humidity increased over water-stressed barrens and short vegetation in northwest China. VWC affected EDVI-ET under clouds in temperate transitional zones from flatlands to highlands. This study highlighted the importance of cloud impacts in satellite ET estimation.

Research areas: Land Surface/Agriculture Indicators
An efficient method for global parameter sensitivity analysis and its applications to the Australian community land surface model (CABLE) (2013)

State-of-the-art global land surface models (LSMs) have a large number (i.e. a few hundred) of parameters. Many of those parameters are poorly constrained and are therefore very uncertain. Usually only a few of the parameters are responsible for changes in the model output of interest. Identifying those parameters that have a significant effect on the model output is an important step before applying parameter estimation methods using observations. However this has not been done systematically for any global LSMs yet, because of the computational costs involved. Here, we introduce a global sensitivity analysis method that is widely used in chemical engineering. This method includes two steps: a screening step that ranks all model parameters by their importance on model output in order to select the potentially important parameters and a second step that aims to quantify the contribution to the variance of model output by each of the pre-selected parameters and by their interactions. This method can be readily applied to any model. Here we apply this method to the Australian community land surface model (CABLE) as an example, and find that the two-step approach is efficient as only 690 model simulations are required to identify the few important parameters amongst the 22 parameters for each of the 10 plant functional types (PFTs) in the first step. Another 256 model simulations are required for the variance based analysis in the second step. We find that the leaf maximum carboxylation rate (vcmax) is by far the most important parameter for global annual gross primary productivity (GPP) across all PFTs. However, if focusing on annual latent heat flux (LE) the importance of the parameters is very much PFT dependent. We suggest that this two-step approach should be used to identify important parameters in global LSMs, so that observations to constrain parameters can be used more efficiently in a subsequent parameter estimation step.

Research areas: Validation
Temperature and salinity variability in the SODA3, ECCO4r3, and ORAS5 ocean reanalyses, 1993–2015 (2019)

Abstract This study extends recent ocean reanalysis comparisons to explore improvements to several next-generation products, the Simple Ocean Data Assimilation, version 3 (SODA3); the Estimating the Circulation and Climate of the Ocean, version 4, release 3 (ECCO4r3); and the Ocean Reanalysis System 5 (ORAS5), during their 23-yr period of overlap (19932015). The three reanalyses share similar historical hydrographic data, but the forcings, forward models, estimation algorithms, and bias correction methods are different. The study begins by comparing the reanalyses to independent analyses of historical SST, heat, and salt content, as well as examining the analysis-minus-observation misfits. While the misfits are generally small, they still reveal some systematic biases that are not present in the reference Hadley Center EN4 objective analysis. We next explore global trends in temperature averaged into three depth intervals: 0300, 3001000, and 10002000 m. We find considerable similarity in the spatial structure of the trends and their distribution among different ocean basins; however, the trends in global averages do differ by 30%40%, which implies an equivalent level of disagreement in net surface heating rates. ECCO4r3 is distinct in having quite weak warming trends while ORAS5 has stronger trends that are noticeable in the deeper layers. To examine the performance of the reanalyses in the Arctic we explore representation of Atlantic Water variability on the Atlantic side of the Arctic and upper-halocline freshwater storage on the Pacific side of the Arctic. These comparisons are encouraging for the application of ocean reanalyses to track ocean climate variability and change at high northern latitudes.

Research areas: Validation