nasa-gesdisc/es-publications-researchareas huggingface dataset:
Southeast Asian dust flux reconstructed using accurately dated stalagmite thorium concentrations (2022)

Mineral dust flux is an important component of the global climate system, but chronological uncertainties complicate interpretations of the timing and magnitude of paleo-dust fluxes. This study establishes a correlation between modern satellite-derived atmospheric dust concentration data and stalagmite thorium concentrations (from published records of stalagmites growing over the last 500 years), and subsequently applies this relationship to reconstruct Asian dust flux during the late Pleistocene using published records. Thorium is an element often associated with mineral dust, and because most stalagmite records are dated using uranium-thorium disequilibrium techniques, a spatiotemporally large, yet untapped, database of thorium concentrations exists. Specifically, 194 thorium records from South-East (SE) Asian stalagmites were compiled to produce a composite SE Asian dust flux record for the past 100,000 years. The new dust flux record is consistent with the perspective that dust flux is a function of westerly wind strength across Asia and regional aridity. Dansgaard-Oeschger (D-O) event expression within the median stalagmite 232Th record is variable, with several D-O events evident within stalagmite 18O records not evident within the new dust record. This may reflect East Asian Summer Monsoon (EASM) moisture source region Sea Surface Temperature (SST) variability (affecting stalagmite 18O) but stable westerly wind regime (resulting in invariant 232Th concentrations) or the fact that these events were too brief to trigger biome shifts. This study presents encouraging first results suggesting that stalagmite thorium concentrations partially reflect dust flux over cave sites. Although here we use a compilation of records, and the spread of data within each time slice is high, future studies at well-understood cave sites where thorium input to the cave is predominantly via dry deposition may require only single stalagmites to produce adequate absolutely dated dust reconstructions.

Research areas: Air Quality
Remote sensing of aerosols due to biomass burning over Kanpur, Sao-Paulo, Ilorin and Canberra (2022)

Aerosol affect the climate in number of ways. In order to investigate these effects, we need a deep insight into aerosols optical, physical and radiative properties. So, to understand aerosols climatology, we investigate the properties of aerosols such as aerosol optical depth (AOD) (500 nm), Angstrom exponent (AE) (440870 nm), single scattering albedo (SSA), refractive index (RI) and aerosols radiative forcing (ARF). For this purpose, we select four different AErosol RObotic NETwork (AERONET) sites located in four different continents; Kanpur, (India) Asia, Sao-Paulo, (Brazil) Southern America, IIorin, (Nigeria) Africa and Canberra, Australia. High AOD and AE is found (AOD = 0.90, AE = 1.31) in November at Kanpur and in September (AOD = 0.39, AE = 1.48) at Sao-Paulo. High AOD (1.06 and 1.12) over IIorin in January and February is found because of fog and haze. SSA shows decreasing trend with increasing wavelengths having minimum value (0.88 and 0.78 at 1020 nm) during the months of DJF and SON over Sao-Paulo and Canberra respectively. The highest value of SSA (~ 0.96) is found during the months of MAM over IIorin because of presence of coarse aerosols. The low value of SSA over Kanpur during DJF months shows dominance of fine urban/ biomass burning aerosols. Based on the values of AOD, AE and SSA, Canberra is the most pristine site. The estimated ARF values indicate that Kanpur and Ilorin sites exhibit higher TOA and BOA values as compared to Sao-Paulo. ARF at ATM is observed to be 7.4 Wm2 higher during JJA months and 10.1 Wm2 during SON months than MAM months over Kanpur. We have also observed lowest ARF efficiency (FeffBOA) of  181 Wm2 AOD1550 nm during MAM months for Sao-Paulo while the highest value of  297 Wm2 AOD1550 nm is observed during DJF months for Kanpur.

Research areas: Environmental Impacts, Air Quality
Apple yield prediction mapping using machine learning techniques through the Google Earth Engine cloud in Kashmir Valley, India (2023)

Our study established a machine learning (ML) model that could predict the apple yield based on various satellite multisensor data, such as climatological, SAR backscatter, terrain distribution, and soil factors, grouped as 26 subcriteria. A total of 986 apple orchards database were collected from 2018 to 2021 in Kashmir Valley, India covering an area of 277953.7 ha farmland. The novelty of our research is the integration of Google Earth Engine cloud and ML models, namely random forest, support vector machine, extreme gradient boosting, K-nearest neighbors, and Cubist along with the geographic information system/remote sensing technology to create an accurate and comprehensive apple yield prediction model in the precision agriculture realm for highlands. The multicollinearity testing indicated that the tolerance and VIF values of all the conditioning factors were <0.1 and <6.85, respectively, indicating no multicollinearity problems among the apple yield suitability factors. Among the tested ML models, the Cubist model performed best, with R2 of 0.83, root-mean-squared error of 0.56 t / ha, and mean absolute error of 0.2 t / ha. The results showed a low mean fruit yield during 2018 of 12.36 ton / ha, whereas maximum fruit yield was reflected in 2021 of 14.05 ton / ha. The heat map revealed the highest normalized differential vegetation index along with vertical-vertical/ vertical-horizontal polarization backscatter, detected during the pre-event of severe snowfall compared to on- and postevent of snowfall for the respective years. Untimely snowing and infestation due to fungi and bacterial diseases regularly reduce fruit yield in the study area. Our study successfully used of high-resolution optical-SAR data combined with ML models as a promising tool for monitoring the yield variability over the highland areas.

Research areas: Agriculture, Atmospheric/Ocean Indicators
Climate-driven acceleration in forest evapotranspiration fuelling extreme rainfall events in the Himalaya (2021)

Warming-induced expansion in vegetation coverage and activity can accelerate the montane hydrological regimes. However, the climate impacts on ecohydrology of forested valleys of the Himalaya are uncertain. In this study, utilizing results of about three centuries of cellulose isotope chronologies ( 13C and  18O) of dominant tree species, geo-chronological proxies, bio-geophysical dataset and simulations including satellite observations, we show an activation in the ecophysiological processes including evapotranspiration (ET) since the 1950s. Observation suggests rapid greening, while isotopic records indicate enhanced assimilation and transpiration in deciduous species vis-a-vis conifers post 1950s. Given strong vegetation-precipitation feedback and superimposed on the increasing trends of conducive atmospheric factors affecting valley-scale convective processes, intensification in forest ET is manifesting in a progressive enhancement in extreme rainfall events (EREs) since the last few decades. Results suggest that representation of ecophysiological processes and dynamics of seasonal moisture loading in observational and modelling framework is critical for understanding EREs under climate change.

Research areas: Land Surface/Agriculture Indicators, Atmospheric/Ocean Indicators
Investigation of the Vegetation Coverage Dynamics and its Relation to Atmospheric Patterns in Kabul River Basin in Afghanistan (2022)

The variations in vegetation coverage (defined as the area with Normalised Difference Vegetation Index (NDVI) > 0.2) and atmospheric patterns occurring during various vegetation seasons in the Kabul River Basin (KRB) in Afghanistan during 20012019 were analyzed. The analysis was done based on the NDVI, land surface temperature (LST), precipitation images from the remote sensing data, and geopotential height and temperature at 500 hPa from the retrospective datasets. The results revealed that the vegetation dynamics in KRB are impacted by both precipitation and LST. In the winter season, the LST has a more substantial role in shaping the vegetation dynamics than precipitation, while it is on contrary during the summer season. Cluster analysis showed that the four atmospheric patterns (e.g., Sub-Tropical High Pressure (STHP), Western European ridge-the Eastern Mediterranean and the Black Sea trough, Caspian Sea ridge (CSR), and the Mediterranean Sea trough-Central to Eastern Iran trough) can be identified and connected with the periods with the highest and the lowest vegetation coverage (VC) anomalies in the study area. The CSR and the Mediterranean Sea trough-the Central to Eastern Iran trough are the patterns responsible for the most positive VC anomalies. At the same time, the STHP and Western Europe ridge-the Eastern Mediterranean and the Black Sea trough are responsible for the most negative VC anomalies. As the atmospheric patterns have a significant role in shaping the vegetation status, a quick alert system to prevent agricultural areas from water or temperature stresses can be developed based on observations of them.

Research areas: Land Surface/Agriculture Indicators
Correlation of Ground-Based and Satellite Measurements of Methane Concentration in the Surface Layer of the Atmosphere in the Tiksi Region (2022)

In this work, the correlation of AIRS/Aqua satellite measurements of methane concentration in the surface layer of the atmosphere with ground-based measurements at the Voeikov Main Geophysical Observatory in Tiksi in 20112020 is determined. The correlation of satellite measurements with ground measurements depends on the season. In spring and summer, the correlation exceeds the autumn correlation, and it is lowest in winter. For the winter period (December, January, and February), the decrease in correlation is associated with possible inversions of air temperature (Yurganov et al., 2016; Anisimov and Kokorev, 2015). When the temperature contrast (TC) is less than 10C (the difference between air temperatures at the surface and at a level of 4 km (600 hPa)), it is preferable to use satellite data averaged at the levels of 400500 hPa. This gives an increase in the Spearman correlation coefficient (SCC) between ground and satellite measurements from 0.44 (1000 hPa) to 0.63 (400500 hPa). However, the obtained regression relationship with a determination coefficient of 0.44 makes it problematic to use it for predicting ground-based data from satellite measurements in areas similar in climatic conditions to Tiksi.

Research areas: Validation
Sensitivity to initial conditions on the simulation of extratropical cyclone Gong formed over North Atlantic (2021)

Sensitivity of initial and boundary conditions (ICs) are studied for an extra-tropical cyclone (ETC, Gong (2013)) over North Atlantic Ocean using WRF model.Movement (track) and synoptic feature of Gong have been evaluated with observations. WRF could simulate the characteristics throughout life cycle of Gong.The model attained intense stage was well simulated.Cyclogenesis and Q-Vector for Gong are sensitive to the ICs and model could simulate better with 48-h lead time.

Research areas: Severe Storms
Surface Shortwave Radiative Fluxes derived from the US Air Force Cloud Depiction Forecast System World-Wide Merged Cloud Analysis (2022)

Abstract We present a global scale evaluation of surface shortwave (SW) radiative fluxes as derived with cloud amount information from the US Air Force Cloud Depiction Forecast System (CDFS) II World-Wide Merged Cloud Analysis (WWMCA) and implemented in the framework of the NASA Land Information System (LIS). Evaluation of this product is done against ground observations, a satellite-based product from the Moderate Resolution Imaging Spectroradiometer (MODIS), and several reanalysis outputs. While the LIS/US Air Force (USAF) product tends to overestimate the SW fluxes when compared to ground observations and satellite estimates, its performance is comparable or better than the following reanalysis products: ERA5, CFSR and MERRA-2. Results are presented using all available observations over the globe and independently for several regional domains of interest. When evaluated against ground observations over the globe the bias in the LIS/USAF product at daily time scale was about 9.34 Wm2 and the rms was 29.20 Wm2 while over the USA the bias was about 10.65 Wm2 and the rms was 35.31 Wm2, respectively. The sample sizes used were not uniform over the different regions and the quality of both ground truth and the outputs of the other products may vary regionally. It is important to note that the LIS/USAF is a NearReal-Time (NRT) product of interest for potential users and as such fills a need that is not met by most products. Due to latency issues, the level of observational inputs in the NRT product is less than in the reanalysis data.

Research areas: Validation
Flood modeling through remote sensing datasets such as LPRM soil moisture and GPM-IMERG precipitation: A case study of ungauged basins across Morocco (2023)

North Africa is characterized by several ungauged basins, especially in Morrocco, where satellite products could be an alternative of the lack ground-based measurements. In this study, the Land Parameter Retrieval Model (LPRM) soil moisture and the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (GPM-IMERG) were used in flood modeling in Moroccan ungauged basins (Bourrous, Al Wiza, El Hallouf and Jamala). We started with comparing GPM-IMERG Early with ground measurements from five rain gauges. Next, the Soil Conservation Service  Curve Number (SCS-CN) was applied with ground precipitation measurements, LPRM and GPM-IMERG Early datasets to simulated flood events in a gauged basin, the Ghdat. Finally, this SCS-CN model was transposed with these satellite data sets validated to those ungauged basins in order to reproduce flood events. The results show that the GPM-IMERG Early is best with in situ measurements (correlation coefficient = 0.50; relative bias = 27.51%; probability of detection = 0.77; false alarm rate = 0.23), on a daily scale. The observed precipitation, LPRM and GPM-IMERG Early were performed well in validation to simulate floods in the Ghdat, where NashSutcliffe criterion range from 0.43 and 0.98 using the SCS-CN model. For Bourrous, Al Wiza, El Hallouf and Jamala all flood events and hydrographs were reproduced by GPM-IMERG Early and LPRM products. Furthermore, LPRM products were validated against soil moisture measurements with a coefficient of determine R2 between 0.72 and 0.84. The results of this work provided interesting insights for flood modeling using GPM-IMERG and LPRM satellite products in ungauged basins.

Research areas: Floods
PM2.5-bound biogenic secondary organic aerosol tracers over a regionally representative site in central India: Characteristics and sources (2023)

Biogenic secondary organic aerosol (BSOA) in PM2.5 over Bhopal, central India were quantified. Fine particles were collected on Teflon filters for measuring PM2.5 mass and on quartz fiber filter for organic carbon (OC), elemental carbon (EC), and BSOA tracers, including oxidation products of isoprene, monoterpene and sesquiterpene between November 2017 and May 2018. Isoprene SOA tracers were present in the highest mean concentration (86.5 ng m3  93.5 ng m3) amongst all quantified tracers, accounting for 64%  23% of the BSOA tracers. Isoprene oxidation products, including C5-alkene triols and 2-methyltetrols, and sesquiterpene oxidation products (-caryophyllinic acid), were found to be the most abundant compound class over the study location. Correlations in the temporal variation of tracers of isoprene and monoterpene, with ozone (O3) suggested that photochemical oxidation influenced their concentrations over central India. The temporal variation of -caryophyllinic acid and comparison with the previous suggested that their concentration was potentially influenced by biomass burning. Overall, the findings suggest that oxidation products of isoprene, monoterpene and sesquiterpene can be utilized to understand the characteristics and apportionment of BSOA sources over central India.

Research areas: Air Quality, Environmental Impacts
Evidence against Rapid Mercury Oxidation in Photochemical Smog (2022)

Mercury pollution is primarily emitted to the atmosphere, and atmospheric transport and chemical processes determine its fate in the environment, but scientific understanding of atmospheric mercury chemistry is clouded in uncertainty. Mercury oxidation by atomic bromine in the Arctic and the upper atmosphere is well established, but less is understood about oxidation pathways in conditions of anthropogenic photochemical smog. Many have observed rapid increases in oxidized mercury under polluted conditions, but it has not been clearly demonstrated that these increases are the result of local mercury oxidation. We measured elemental and oxidized mercury in an area that experienced abundant photochemical activity (ozone >100 ppb) during winter inversion (i.e., cold air pools) conditions that restricted entrainment of air from the oxidized mercury-rich upper atmosphere. Under these conditions, oxidized mercury concentrations decreased day-upon-day, even as ozone and other pollutants increased dramatically. A box model that incorporated rapid kinetics for reactions of elemental mercury with ozone and OH radical overestimated observed oxidized mercury, while incorporation of slower, more widely accepted reaction rates did not. Our results show that rapid gas-phase mercury oxidation by ozone and OH in photochemical smog is unlikely.

Research areas: Air Quality
Aerosol Characterization of Northern China and Yangtze River Delta Based on Multi-Satellite Data: Spatiotemporal Variations and Policy Implications (2023)

Horizontal and vertical distributions of aerosol properties in the Taklimakan Desert (TD), North central region of China (NCR),North China Plain(NCP), and Yangtze River Delta (YRD) were investigated by statistical analysis using Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) L3 data from 2007 to 2020, to identify the similarities and differences in atmospheric aerosols in different regions, and evaluate the impact of pollution control policies developed in China in 2013 on aerosol properties in the study area. The aerosol optical depth (AOD) distribution had substantial seasonal and spatial distribution characteristics. AOD had high annual averages in TD (0.38), NCP (0.49), and YRD (0.52). However, these rates showed a decline post-implementation of the long-term pollution control policies; AOD values declined by 5%, 13.8%, 15.5%, and 23.7% in TD, NCR, NCP, and YRD respectively when comparing 20142018 to 20072013, and by 7.8%, 11.5%, 16%, and 10.4% when comparing 20192020 to 20142018. The aerosol extinction coefficient showed a clear regional pattern and a tendency to decrease gradually as height increased. Dust and polluted dust were responsible for the changes in AOD and extinction coefficients between TD and NCR and NCP and YRD, respectively. In TD, with change of longitude, dust aerosol first increased and then decreased gradually, peaking in the middle. Similarly in NCP, polluted dust aerosol first increased and then decreased, with a maximum value in the middle. The elevated smoke aerosols of NCP and YRD were significantly higher than those observed in TD and NCR. The high aerosol extinction coefficient values (>0.1 km1) were mainly distributed below 4 km, and the relatively weak aerosol extinction coefficients (>0.001 km1) were mainly distributed between 58 km, indicating that the high-altitude long-range transport of TD and NCR dust aerosols affects NCP and YRD.

Research areas: Air Quality
Interannual variability of ice cover in the Caspian Sea (2022)

Satellite remote sensing data (SAR and Ocean Color), MERRA-2 reanalysis and records at Astrakhan meteorological station were used to investigate interannual variability of ice cover characteristics in the North Caspian Sea for 23 winter seasons (November 1  April 15) from 1999/2000 to 2021/2022. The maximum annual ice cover area, ice freeze onset and melt dates and ice cover duration were determined from satellite remote sensing data, mostly SAR instruments on board the European Space Agencys satellites, ranging from ERS-2 to the Sentinel-1A, -1B tandem. We propose a new band combination for Sentinel-2 MSI and Landsat-8 OLI that allows better distinguishing ice cover from clouds or land than the standard RGB composites. In the absence of SAR data, this method was used to estimate the above mentioned parameters with high spatial and temporal resolution. To assess the severity of winters, the criterion on the basis of the sum of freezing degree-days (SFDD) was applied. For this purpose, we used values of daily minimum air temperature over the North Caspian (44.4647.14N, 46.7052.90E), daily mean and daily minimum ones over its coldest eastern part (with the western border at 50E), obtained from the MERRA-2 reanalysis, as well as data from the meteorological station in Astrakhan (46.35N, 48.07E). The resulting SFDD sequences show that until the winter of 2011/2012, there was a cooling trend on average (with noticeable interannual variability), whereas after that winter it changed to warming for Astrakhan and virtually disappeared for the North Caspian and its eastern part. A noticeable interannual variability is also shown by the maximum ice area and the duration of the ice period, both parameters with maximums in the winter of 2011/2012. We discuss in detail the correspondence between the SFDD and ice cover characteristics variations, as well as previously published results. In agreement with the other authors, we find that in the 21st century, compared to the 20th century, the number of very severe and severe winters has decreased, while the number of mild winters has increased.

Research areas: Cryospheric Indicators