nasa-gesdisc/es-publications-researchareas huggingface dataset:
OClO as observed by TROPOMI: a comparison with meteorological parameters and polar stratospheric cloud observations (2022)

Abstract. Chlorine dioxide (OClO) is a by-product of the ozone-depleting halogen chemistry in the stratosphere. Although it is rapidly photolysed at low solar zenith angles (SZAs), it plays an important role as an indicator of the chlorine activation in polar regions during polar winter and spring at twilight conditions because of the nearly linear dependence of its formation on chlorine oxide (ClO). Here, we compare slant column densities (SCDs) of chlorine dioxide (OClO) retrieved by means of differential optical absorption spectroscopy (DOAS) from spectra measured by the TROPOspheric Monitoring Instrument (TROPOMI) with meteorological data for both Antarctic and Arctic regions for the first three winters in each of the hemispheres (November 2017October 2020). TROPOMI, a UVVisNIRSWIR instrument on board of the Sentinel-5P satellite, monitors the Earth's atmosphere in a near-polar orbit at an unprecedented spatial resolution and signal-to-noise ratio and provides daily global coverage at the Equator and thus even more frequent observations at polar regions. The observed OClO SCDs are generally well correlated with the meteorological conditions in the polar winter stratosphere; for example, the chlorine activation signal appears as a sharp gradient in the time series of the OClO SCDs once the temperature drops to values well below the nitric acid trihydrate (NAT) existence temperature (TNAT). Also a relation of enhanced OClO values at lee sides of mountains can be observed at the beginning of the winters, indicating a possible effect of lee waves on chlorine activation. The dataset is also compared with CALIPSO Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) polar stratospheric cloud (PSC) observations. In general, OClO SCDs coincide well with CALIOP measurements for which PSCs are detected. Very high OClO levels are observed for the northern hemispheric winter 2019/20, with an extraordinarily long period with a stable polar vortex being even close to the values found for southern hemispheric winters. An extraordinary winter in the Southern Hemisphere was also observed in 2019, with a minor sudden stratospheric warming at the beginning of September. In this winter, similar OClO values were measured in comparison to the previous (usual) winter till that event but with a OClO deactivation that was 12 weeks earlier.

Research areas: Air Quality
A machine-learning-based cloud detection and thermodynamic-phase classification algorithm using passive spectral observations (2020)

Abstract. We trained two Random Forest (RF) machine learning models for cloud mask and cloud thermodynamic-phase detection using spectral observations from Visible Infrared Imaging Radiometer Suite (VIIRS) on board Suomi National Polar-orbiting Partnership (SNPP). Observations from Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) were carefully selected to provide reference labels. The two RF models were trained for all-day and daytime-only conditions using a 4-year collocated VIIRS and CALIOP dataset from 2013 to 2016. Due to the orbit difference, the collocated CALIOP and SNPP VIIRS training samples cover a broad-viewing zenith angle range, which is a great benefit to overall model performance. The all-day model uses three VIIRS infrared (IR) bands (8.6, 11, and 12 m), and the daytime model uses five Near-IR (NIR) and Shortwave-IR (SWIR) bands (0.86, 1.24, 1.38, 1.64, and 2.25 m) together with the three IR bands to detect clear, liquid water, and ice cloud pixels. Up to seven surface types, i.e., ocean water, forest, cropland, grassland, snow and ice, barren desert, and shrubland, were considered separately to enhance performance for both models. Detection of cloudy pixels and thermodynamic phase with the two RF models was compared against collocated CALIOP products from 2017. It is shown that, when using a conservative screening process that excludes the most challenging cloudy pixels for passive remote sensing, the two RF models have high accuracy rates in comparison to the CALIOP reference for both cloud detection and thermodynamic phase. Other existing SNPP VIIRS and Aqua MODIS cloud mask and phase products are also evaluated, with results showing that the two RF models and the MODIS MYD06 optical property phase product are the top three algorithms with respect to lidar observations during the daytime. During the nighttime, the RF all-day model works best for both cloud detection and phase, particularly for pixels over snow and ice surfaces. The present RF models can be extended to other similar passive instruments if training samples can be collected from CALIOP or other lidars. However, the quality of reference labels and potential sampling issues that may impact model performance would need further attention.

Research areas: Validation
A Convolution Neural Network-based Method for Sea Ice Remote Sensing using GNSS-R Data (2022)

Sea ice remote sensing is of great significance to the understanding of polar climate change. At present, the global navigation satellite system reflector (GNSS-R) technology has been applied to the study of sea ice remote sensing and achieved good results. In this paper, a convolution neural network (CNN) based method for sea ice recognition (SIR) and estimation of sea ice concentration (SIC) using GNSS-R data is proposed. Specifically, a CNN model is designed to solve the classification problem of SIR and the regression problem of SIC estimation. In the stage of data set construction, first, the global GNSS-R data (TDS-l), in a certain period of time, is spatiotemporally matched with the relatively reliable sea ice data (NSIDC), and then the matched GNSS-R data is extracted to balance the amount of seawater data and sea ice data. In the stage of CNN model construction, the feature learning ability of the model is enhanced by adding convolution layer, pooling layer and full connection layer. Simulation results show that the proposed CNN -based scheme has a higher prediction accuracy of SIR and lower estimation error of SIC than other existing methods.

Research areas: Cryospheric Indicators
Replacing missing values in the standard Multi-angle Imaging SpectroRadiometer (MISR) radiometric camera-by-camera cloud mask (RCCM) data product (2020)

Abstract. The Multi-angle Imaging SpectroRadiometer (MISR) is one of the five instruments hosted on board the NASA Terra platform, launched on 18 December 1999. This instrument has been operational since 24 February 2000 and is still acquiring Earth observation data as of this writing. The primary mission of the MISR is to document the state and properties of the atmosphere, in particular the clouds and aerosols it contains, as well as the planetary surface, on the basis of 36 data channels collectively gathered by its nine cameras (pointing in different directions along the orbital track) in four spectral bands (blue, green, red and near-infrared). The radiometric camera-by-camera cloud mask (RCCM) is derived from the calibrated measurements at the nominal top of the atmosphere and is provided separately for each of the nine cameras. This RCCM data product is permanently archived at the NASA Atmospheric Science Data Center (ASDC) in Hampton, VA, USA, and is openly accessible (Diner et al., 1999b, and https://doi.org/10.5067/Terra/MISR/MIRCCM_L2.004). For various technical reasons described in this paper, this RCCM product exhibits missing data, even though an estimate of the clear or cloudy status of the environment at each individual observed location can be deduced from the available measurements. The aims of this paper are (1) to describe how to replace over 99 % of the missing values by estimates and (2) to briefly describe the software to replace missing RCCM values, which is openly available to the community from the GitHub website, https://github.com/mmverstraete/MISR\ RCCM/ (last access: 12 March 2020), or https://doi.org/10.5281/ZENODO.3240017 (Verstraete, 2019e). Two additional sets of resources are also made available on the research data repository of GFZ Data Services in conjunction with this paper. The first set (A; Verstraete et al., 2020; https://doi.org/10.5880/fidgeo.2020.004) includes three items: (A1) a compressed archive, RCCM_Out.zip, containing all intermediary, final and ancillary outputs created while generating the figures of this paper; (A2) a user manual, RCCM_Out.pdf, describing how to install, uncompress and explore those files; and (A3) a separate input MISR data archive, RCCM_input_68050.zip, for Path 168, Orbit 68050. This latter archive is usable with (B), the second set (Verstraete and Vogt, 2020; https://doi.org/10.5880/fidgeo.2020.008), which includes (B1), a stand-alone, self-contained, executable version of the RCCM correction codes, RCCM_Soft_Win.zip, using the IDL Virtual Machine technology that does not require a paid IDL license, as well as (B2), a user manual, RCCM_Soft_Win.pdf, to explain how to install, uncompress and use this software.

Research areas: Validation
An Algorithm to Bias-Correct and Transform Arctic SMAP-Derived Skin Salinities into Bulk Surface Salinities (2022)

An algorithmic approach, based on satellite-derived sea-surface (skin) salinities (SSS), is proposed to correct for errors in SSS retrievals and convert these skin salinities into comparable in-situ (bulk) salinities for the top-5 m of the subpolar and Arctic Oceans. In preparation for routine assimilation into operational ocean forecast models, Soil Moisture Active Passive (SMAP) satellite Level-2 SSS observations are transformed using Argo float data from the top-5 m of the ocean to address the mismatch between the skin depth of satellite L-band SSS measurements (1 cm) and the thickness of top model layers (typically at least 1 m). Separate from the challenge of Argo float availability in most of the subpolar and Arctic Oceans, satellite-derived SSS products for these regions currently are not suitable for assimilation for a myriad of other reasons, including erroneous ancillary air-sea forcing/flux products. In the subpolar and Arctic Oceans, the root-mean-square error (RMSE) between the SMAP SSS product and several in-situ salinity observational data sets for the top-5 m is greater than 1.5 pss (Practical Salinity Scale), which can be larger than their temporal variability. Thus, we train a machine-learning algorithm (called a Generalized Additive Model) on in-situ salinities from the top-5 m and an independent air-sea forcing/flux product to convert the SMAP SSS into bulk-salinities, correct biases, and quantify their standard errors. The RMSE between these corrected bulk-salinities and in-situ measurements is less than 1 pss in open ocean regions. Barring persistently problematic data near coasts and ice-pack edges, the corrected bulk-salinity data are in better agreement with in-situ data than their SMAP SSS equivalent.

Research areas: Validation
A Microphysics-scheme Consistent Snow Optical Parameterization for the Community Radiative Transfer Model (2022)

Abstract The satellite observational data assimilation community requires consistent hydrometer descriptionsincluding mass-size relation and particle size distributionto be used in both the forecast model and observation operator. We develop a microphysics scheme-consistent snow and graupel single-scattering property database to meet this requirement. In this database, snowflakes are modeled as a mixture of small column and large aggregated ice particles, the mixing ratios of which may be adjusted to satisfy a given mass-size relation. Snow single-scattering properties are computed for four different mass-size relations. Subsequently, the snow description in the Thompson microphysics scheme is used as an example to demonstrate how microphysics scheme-consistent snow bulk optical properties are derived. The Thompson scheme-consistent snow bulk optical properties are added to the Community Radiative Transfer Model (CRTM) version 2.4.0. With CloudSat Cloud Profiling Radar (CPR) snow and liquid precipitation retrievals as the inputs, CRTM simulations are performed over global oceans and compared with four collocated Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channel observations. The CRTM simulated brightness temperatures show agreement with the GMI observed brightness temperatures in cases of light-to-moderate precipitation over extratropical and polar ice-free oceans, with root mean square errors (RMSEs) of 4.3, 13.0, 1.8, and 3.3 K in the 166 GHz (vertical polarization), 166 GHz (horizontal polarization), 1833 GHz (vertical polarization), and 1837 GHz (vertical polarization) channels, respectively. The result demonstrates the potential of using the newly developed microphysics scheme-consistent snow optical parameterization in data assimilation applications.

Research areas: Validation
An aggregate learning approach for interpretable semi-supervised population prediction and disaggregation using ancillary data (2020)

Census data provide detailed information about population characteristics at a coarse resolution. Nevertheless, fine-grained, high-resolution mappings of population counts are increasingly needed to characterize population dynamics and to assess the consequences of climate shocks, natural disasters, investments in infrastructure, development policies, etc. Disaggregating these census is a complex machine learning, and multiple solutions have been proposed in past research. We propose in this paper to view the problem in the context of the aggregate learning paradigm, where the output value for all training points is not known, but where it is only known for aggregates of the points (i.e. in this context, for regions of pixels where a census is available). We demonstrate with a very simple and interpretable model that this method is on par, and even outperforms on some metrics, the state-of-the-art, despite its simplicity.

Research areas: Validation
Groundwater Storage Loss Associated With Land Subsidence in Western (2020)

Land subsidence caused by groundwater extraction has numerous negative consequences, such as loss of groundwater storage and damage to infrastructure. Understanding the magnitude, timing, and locations of land subsidence, as well as the mechanisms driving it, is crucial to implementing mitigation strategies, yet the complex, nonlinear processes causing subsidence are difficult to quantify. Physical models relating groundwater flux to aquifer compaction exist but require substantial hydrological data sets and are time consuming to calibrate. Land deformation can be measured using interferometric synthetic aperture radar (InSAR) and GPS, but the former is computationally expensive to estimate at scale and is subject to tropospheric and ionospheric errors, and the latter leaves many temporal and spatial gaps. In this study, we apply for the first time a machine learning approach that quantifies the relationships of various widely available input data, including evapotranspiration, land use, and sediment thickness, with land subsidence. We apply this method over the Western United States and estimate that from 2015 to 2016, ~2.0 km<SUP>3</SUP>/yr of groundwater storage was lost due to groundwater pumping-induced compaction of sediments. Subsidence is concentrated in the Central Valley of California, and the state of California accounts for 75% of total subsidence in the Western United States. Other significant areas of subsidence occur in cultivated regions of the Basin and Range province. This study demonstrates that widely available ancillary data can be used to estimate subsidence at a larger scale than has been previously possible.

Research areas: Land Surface/Agriculture Indicators
Application of Machine Learning to Debris Flow Susceptibility Mapping along the China–Pakistan Karakoram Highway (2020)

The ChinaPakistan Karakoram Highway is an important land route from China to South Asia and the Middle East via Pakistan. Due to the extremely hazardous geological environment around the highway, landslides, debris flows, collapses, and subsidence are frequent. Among them, debris flows are one of the most serious geological hazards on the Karakoram Highway, and they often cause interruptions to traffic and casualties. Therefore, the development of debris flow susceptibility mapping along the highway can potentially facilitate its safe operation. In this study, we used remote sensing, GIS, and machine learning techniques to map debris flow susceptibility along the Karakoram Highway in areas where observation data are scarce and difficult to obtain by field survey. First, the distribution of 544 catchments which are prone to debris flow were identified through visual interpretation of remote sensing images. The factors influencing debris flow susceptibility were then analyzed, and a total of 17 parameters related to geomorphology, soil materials, and triggering conditions were selected. Model training was based on multiple common machine learning methods, including Ensemble Methods, Gaussian Processes, Generalized Linear models, Navies Bayes, Nearest Neighbors, Support Vector Machines, Trees, Discriminant Analysis, and eXtreme Gradient Boosting. Support Vector Classification (SVC) was chosen as the final model after evaluation; its accuracy (ACC) was 0.91, and the area under the ROC curve (AUC) was 0.96. Among the factors involved in SVC, the Melton Ratio (MR) was the most important, followed by drainage density (DD), Hypsometric Integral (HI), and average slope (AS), indicating that geomorphic conditions play an important role in predicting debris flow susceptibility in the study area. SVC was used to map debris flow susceptibility in the study area, and the results will potentially facilitate the safe operation of the highway.

Research areas: Land Surface/Agriculture Indicators
Major geomorphic events and natural hazards during monsoonal precipitation 2018 in the Kali Gandaki Valley, Nepal Himalaya (2021)

Highest geomorphic activity in the central Nepal Himalayas is mainly driven by monsoonal precipitation. In contrast, the northern flanks of the Nepal Himalaya lay in the relative dry rain shadow of the mountain range. During the monsoon 2018, major floods and geomorphic events occurred in the Kali Gandaki (KG) valley impacting both the monsoon-affected and the dry parts of the catchments. We analyze the events and its triggers based on field observations, multiple satellite image interpretation, climatological analysis using Global Precipitation Measurement and MODIS snow cover data, hydrological analysis and media analysis. The hydro-meteorological triggers are complex. Exceptional precipitation in April and May 2018 occurred in the entire study area, followed by a rather dry period. Precipitation in August was exceptional in the northern part whereas below average in the South. We argue that dynamics of snow accumulation and delayed melting contributed significantly to flooding and increased geomorphic activity in the southern part in August whereas flooding in the northern part was mainly triggered by rainfalls. We thus define 2018 as an abnormal (pre-)monsoon year with less rainfall than average but being more catastrophic. Sediment dynamics in the study area are still controlled by the Dhampu rock avalanche dam and the braided river floodplain north of this knickpoint, where sediment pulses delivered from tributaries are rarely connected from the main river. During the monsoon floods 2018 sediment connectivity was given for most tributaries due to the steepness of the catchments. The study area is subject to major human impact. Mostly in the south, numerous hillslopes have been undercut by road construction, leading to higher geomorphic sensitivity. Severe landslides might thus be triggered in future even by less intense rainfall events. Magnitude and frequency of such abnormal (pre-)monsoon precipitation are highly relevant for sediment flux and natural hazards studies.

Research areas: Land Surface/Agriculture Indicators, Atmospheric/Ocean Indicators
Evaluating the effect of ecological policies from the pattern change of persistent green patches–A case study of Yan'an in China's Loess Plateau (2021)

Evaluation from landscape pattern change can comprehensively reflect the impact of ecological policies on the ecosystem service function. However, previous assessments based on landscape patterns only considered land cover type and ignored the quality of vegetation cover, which could hardly reflect regional differences in restoration durability and sustainability. Based on the temporal phase characteristics of the vegetation index, this study proposes the concept of Persistent Green Patches (PGPs). Taking Yan'an, a key area for the implementation of ecological projects in the Loess Plateau, as a study area, the effect of ecological policies was evaluated from the pattern change of PGPs from 2000 to 2017 through Morphological Spatial Pattern Analysis (MSPA). It is found that (1) the area of PGPs was increased from 14.45% to 44.26% in Yan'an since the implementation of the ecological projects; (2) the Grain for Green projects can hardly form a short-term increase of PGPs; while the Natural Forest Protection projects can quickly increase the PGPs area, its effect would tend to be saturated or even decrease in the long term; (3) the fragmentation and connectivity of the landscape show that the ecological projects promote the connection between different green patches, and improves the overall connectivity of the PGPs. This study provides a new perspective on evaluating the effect of ecological projects, which is expected to provide a reference for the future optimization of relevant ecological policies and regional sustainable development.

Research areas: Land Surface/Agriculture Indicators
Human-environment interactions in a Bahamian dune landscape: A (2021)

Investigations were undertaken following the discovery of two Lucayan burials in an Atlantic coast sand dune on Long Island, The Bahamas (site LN‑101), in the aftermath of Hurricane Joaquin in 2015. The dune burials were the first of their kind to be documented and systematically excavated, and they were associated with uncommon Atlantic coast Lucayan sites. We describe the first systematic archaeological prospection and investigation of coastal geomorphology in the region, applying grain‑size analysis to assess the dune's natural history; basic geochemical analysis to detect anthropogenic impacts and determine agricultural potential; radiocarbon dating as a chronological anchor for reconstructing dune development; drone mapping and georeferencing to document landscape trajectories; and the potential of clay‑like soils with respect to pottery production. Significantly, the dune was relatively stable during and after Lucayan occupation, before Hurricane Joaquin stripped about 10 m from the dune face. The results contribute to refined modeling of past and future impacts, especially those attributed to modern climate change, by linking changes in geomorphology to human activities that began over 1000 years ago. The study contributes to a growing body of Caribbean research into the deep‑time impacts of sea‑level change, coastal geomorphology, erosion, climate change, and hurricane activity.

Research areas: Land Surface/Agriculture Indicators