nasa-gesdisc/es-publications-researchareas huggingface dataset:
Changes in Atmospheric, Meteorological, and Ocean Parameters Associated with the 12 January 2020 Taal Volcanic Eruption (2020)

The Taal volcano erupted on 12 January 2020, the first time since 1977. About 35 mild earthquakes (magnitude greater than 4.0) were observed on 12 January 2020 induced from the eruption. In the present paper, we analyzed optical properties of volcanic aerosols, volcanic gas emission, ocean parameters using multi-satellite sensors, namely, MODIS (Moderate Resolution Imaging Spectroradiometer), AIRS (Atmospheric Infrared Sounder), OMI (Ozone Monitoring Instrument), TROPOMI (TROPOspheric Monitoring Instrument) and ground observations, namely, Argo, and AERONET (AErosol RObotic NETwork) data. Our detailed analysis shows pronounced changes in all the parameters, which mainly occurred in the western and south-western regions because the airmass of the Taal volcano spreads westward according to the analysis of airmass trajectories and wind directions. The presence of finer particles has been observed by analyzing aerosol properties that can be attributed to the volcanic plume after the eruption. We have also observed an enhancement in SO2, CO, and water vapor, and a decrease in Ozone after a few days of the eruption. The unusual variations in salinity, sea temperature, and surface latent heat flux have been observed as a result of the ash from the Taal volcano in the south-west and south-east over the ocean. Our results demonstrate that the observations combining satellite with ground data could provide important information about the changes in the atmosphere, meteorology, and ocean parameters associated with the Taal volcanic eruption.

Research areas: Volcanic Eruptions, Earthquakes
Impact of lockdown on air quality in India during COVID-19 pandemic (2020)

First time in India, total lockdown was announced on 22 March 2020 to stop the spread of COVID-19 and the lockdown was extended for 21 days on 24 March 2020 in the first phase. During the total lockdown, most of the sources for poor air quality were stopped in India. In this paper, we present an analysis of air quality (particulate matter-PM2.5, Air Quality Index, and tropospheric NO2) over India using ground and satellite observations. A pronounced decline in PM2.5 and AQI (Air Quality Index) is observed over Delhi, Mumbai, Hyderabad, Kolkata, and Chennai and also a declining trend was observed in tropospheric NO2 concentration during the lockdown period in 2020 compared with the same period in the year 2019. During the total lockdown period, the air quality has improved significantly which provides an important information to the cities administration to develop rules and regulations on how they can improve air quality.

Research areas: Air Quality
A sea level stratospheric ozone intrusion event induced within a thunderstorm gust front (2019)

Ozone from a stratospheric intrusion (SI) reached sea level in association with a thunderstorm gust front during the predawn hours of 16 April 2018. The event caused surface ozone concentration increases of 30 to more than 50 ppbv in a matter of minutes in a band from approximately Richmond, Virginia, to Philadelphia, Pennsylvania. Peak hourly ozone concentrations reached 74 ppbv in northeastern Maryland despite absent photochemistry and ongoing convective activity. An intense jet stream with velocities >80 kt (41 m s(-1)) less than 1 km above ground level was observed associated with a deepening cyclone. Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), showed a filament of ozone with concentrations greater than 90 ppbv extending downward from the stratosphere to the lower troposphere. This SI filament became collocated with an ongoing severe squall line, and stratospheric ozone was transported directly to sea level when entrained into the squall-line gust front. Weather radar and in situ observations confirmed surface ozone increased with the thunderstorm gust front, while a concurrent reduction in carbon monoxide confirmed air within the gust front had stratospheric origins. While rare, such coupling events are important to troposphere-stratosphere exchanges and in overall atmospheric chemistry and climate. This may be the first event of its type and magnitude documented.

Research areas: Severe Storms
New strategies for vertical transport in chemistry transport models: application to the case of the Mount Etna eruption on 18 March 2012 with CHIMERE v2017r4 (2020)

Abstract. Excessive numerical diffusion is one of the major limitations in the representation of long-range transport by chemistry transport models. In the present study, we focus on excessive diffusion in the vertical direction, which has been shown to be a major issue, and we explore three possible ways of addressing this problem: increasing the vertical resolution, using an advection scheme with anti-diffusive properties and more accurately representing the vertical wind. This study was carried out using the CHIMERE chemistry transport model for the 18 March 2012 eruption of Mount Etna, which released about 3 kt of sulfur dioxide into the atmosphere in a plume that was observed by satellite instruments (the Infrared Atmospheric Sounding Interferometer instrument, IASI, and the Ozone Monitoring Instrument, OMI) for several days. The change from the classical Van Leer (1977) scheme to the Despres and Lagoutiere (1999) anti-diffusive scheme in the vertical direction was shown to provide the largest improvement to model outputs in terms of preserving the thin plume emitted by the volcano. To a lesser extent, the improved representation of the vertical wind field was also shown to reduce plume dispersion. Both of these changes helped to reduce vertical diffusion in the model as much as a brute-force approach (increasing vertical resolution).

Research areas: Volcanic Eruptions
A susceptibility-based rainfall threshold approach for landslide occurrence (2019)

Abstract. Rainfall threshold determination is a pressing issue in
the landslide scientific community. While major improvements have been made towards more reproducible techniques for the identification of triggering
conditions for landsliding, the now well-established rainfall intensity or
event-duration thresholds for landsliding suffer from several
limitations. Here, we propose a new approach of the frequentist method for
threshold definition based on satellite-derived antecedent rainfall
estimates directly coupled with landslide susceptibility data. Adopting a
bootstrap statistical technique for the identification of threshold
uncertainties at different exceedance probability levels, it results in
thresholds expressed as AR = ()S(), where AR is antecedent rainfall (mm), S is
landslide susceptibility,  and  are scaling parameters, and
 and  are their uncertainties. The main
improvements of this approach consist in (1) using spatially continuous
satellite rainfall data, (2) giving equal weight to rainfall characteristics
and ground susceptibility factors in the definition of spatially varying
rainfall thresholds, (3) proposing an exponential antecedent rainfall
function that involves past daily rainfall in the exponent to account for
the different lasting effect of large versus small rainfall,
(4) quantitatively exploiting the lower parts of the cloud of data points, most
meaningful for threshold estimation, and (5) merging the uncertainty on
landslide date with the fit uncertainty in a single error estimation. We
apply our approach in the western branch of the East African Rift based on
landslides that occurred between 2001 and 2018, satellite rainfall estimates
from the Tropical Rainfall Measurement Mission Multi-satellite Precipitation
Analysis (TMPA 3B42 RT), and the continental-scale map of landslide
susceptibility of Broeckx et al. (2018) and provide the first regional rainfall
thresholds for landsliding in tropical Africa.

Research areas: Land Surface/Agriculture Indicators
Solar UV radiation measurements in Marambio, Antarctica, during years 20172019 (2020)

Abstract. In March 2017, measurements of downward global irradiance of ultraviolet (UV) radiation were started with a multichannel GUV-2511 radiometer in Marambio, Antarctica (64.23 S; 56.62 W), by the Finnish Meteorological Institute (FMI) in collaboration with the Servicio Meteorologico Nacional (SMN). These measurements were analysed and the results were compared to previous measurements performed at the same site with the radiometer of the Antarctic NILU-UV network during 20002008 and to data from five stations across Antarctica. In 2017/2018 the monthly-average erythemal daily doses from October to January were lower than those averaged over 20002008 with differences from 2.3 % to 25.5 %. In 2017/2018 the average daily erythemal dose from September to March was 1.88 kJ m2, while in 2018/2019 it was 23 % larger (2.37 kJ m2). Also at several other stations in Antarctica the UV radiation levels in 2017/2018 were below average. The maximum UV indices (UVI) in Marambio were 6.2 and 9.5 in 2017/2018 and 2018/2019, respectively, whereas during years 20002008 the maximum was 12. Cloud cover, the strength of the polar vortex and the stratospheric ozone depletion are the primary factors that influence the surface UV radiation levels in Marambio. The lower UV irradiance values in 2017/2018 are explained by the high ozone concentrations in November, February and for a large part of October. The role of cloud cover was clearly seen in December, and to a lesser extent in October and November, when cloud cover qualitatively explains changes which could not be ascribed to changes in total ozone column (TOC). In this study, the roles of aerosols and albedo are of minor influence because the variation of these factors in Marambio was small from one year to the other. The largest variations of UV irradiance occur during spring and early summer when noon solar zenith angle (SZA) is low and the stratospheric ozone concentration is at a minimum (the so-called ozone hole). In 2017/2018, coincident low total ozone column and low cloudiness near solar noon did not occur, and no extreme UV indices were measured.

Research areas: Public Health
UV Index Forecasting under the Influence of Desert Dust: Evaluation against Surface and Satellite-Retrieved Data (2020)

Human exposure to healthy doses of UV radiation is required for vitamin D synthesis, but exposure to excessive UV irradiance leads to several harmful impacts ranging from premature wrinkles to dangerous skin cancer. However, for countries located in the global dust belt, accurate estimation of the UV irradiance is challenging due to a strong impact of desert dust on incoming solar radiation. In this work, a UV Index forecasting capability is presented, specifically developed for dust-rich environments, that combines the use of ground-based measurements of broadband irradiances UVA (320-400 nm) and UVB (280-315 nm), NASA OMI Aura satellite-retrieved data and the meteorology-chemistry mesoscale model WRF-Chem. The forecasting ability of the model is evaluated for clear sky days as well as during the influence of dust storms in Doha, Qatar. The contribution of UV radiation to the total incoming global horizontal irradiance (GHI) ranges between 5% and 7% for UVA and 0.1% and 0.22% for UVB. The UVI forecasting performance of the model is quite encouraging with an absolute average error of less than 6% and a correlation coefficient of 0.93. In agreement with observations, the model predicts that the UV Index at local noontime can drop from 10-11 on clear sky days to approximately 6-7 during typical dusty conditions in the Arabian Peninsula-an effect similar to the presence of extensive cloud cover.

Research areas: Public Health
Application of satellite-based sulfur dioxide observations to support the cleantech sector: Detecting emission reduction from copper smelters (2018)

In this study, we present the result of the application of space-based sulfur dioxide (SO2) observations to evaluate the efficacy of cleantech solutions in reducing air polluting emissions from metal smelting. We analyze the Ozone Monitoring Instrument (OMI) satellite-based SO2 observations over Tsumeb (Namibia) and Bor (Serbia) copper smelters, where two sulfur-capture plants, designed to transform gaseous SO2 emissions into sulfuric acid, were implemented in 2015. We observe a reduction in the annual SO2 emissions by up to 90% after 2015 at both smelters, as a result of the implementation of the sulfuric acid plants. The OMI-based emission estimates are mostly in line with those reported at facility-level and reproduce the same year-to-year variability. This variability is driven by the changes in the copper production, the sulfur-to-copper ratio and by the technology employed to reduce the SO2 emissions. OMI observations are directly used by the company operating the sulfuric acid plants to confirm the efficacy of the employed technology using independent satellite-based observations.

The results demonstrate how satellite-based observations are able to detect relative changes in SO2 emissions and can be used to verify and complete existing emission informations. The approach presented here can be applied to other sources on global scale to support cleantech companies as well as decision-makers involved in environmental policies and sustainable development.

Research areas: Environmental Impacts, Air Quality
Enhancement of carbon monoxide concentration in atmosphere due to large scale forest fire of Uttarakhand (2019)

The richly forested Indian state of Uttarakhand experienced widespread forest fires in April to May 2016. The current study examines dispersion of carbon monoxide (CO) from the source regions of forest fire to distant places, using the Lagrangian particle dispersion model, FLEXPART. Atmospheric Infrared Sounder (AIRS) observations revealed that CO columnar concentrations had increased by almost 28 percentage during 24 April to 02 May 2016 with respect to the previous non-burning period of April 2016 at Uttarakhand. It is also seen that there is considerable enhancement of 45 percentage in average columnar concentration of CO during the burning period, compared to that in the previous 5 years as observed by AIRS. In the present study, concentrations of CO at different pressure levels and columnar CO over Uttarakhand during the forest fire event have been simulated using FLEXPART. The area averaged profile of model derived CO has been compared with the profile from AIRS onboard Aqua. Comparison between model derived columnar CO and satellite observations shows good agreement with coefficient of correlation (r) approximately 0.91 over the burnt areas. Further analysis using FLEXPART reveals that the transport of pollutants is towards north-eastern and eastern regions from the locations of forest fire events. Model derived vertical distribution of CO over Tibet, which is situated at the northeast of Uttarakhand, shows significant increase of CO concentration at higher altitudes around 3 kin from the mean sea level during the fire event. FLEXPART results show that the emissions from the Uttarakhand fires were transported to Tibet during the study period.

Research areas: Wildfires, Air Quality
Cloud feedbacks in extratropical cyclones: insight from long-term satellite data and high-resolution global simulations (2019)

Abstract. A negative extratropical shortwave cloud feedback driven by changes in cloud
optical depth is a feature of global climate models (GCMs). A robust positive
trend in observed liquid water path (LWP) over the last two decades across
the warming Southern Ocean supports the negative shortwave cloud feedback
predicted by GCMs. This feature has been proposed to be due to transitions
from ice to liquid with warming. To gain insight into the shortwave cloud
feedback we examine extratropical cyclone variability and the response of
extratropical cyclones to transient warming in GCM simulations. Multi-Sensor
Advanced Climatology Liquid Water Path (MAC-LWP) microwave observations of
cyclone properties from the period 19922015 are contrasted with GCM
simulations, with horizontal resolutions ranging from 7 km to hundreds of
kilometers. We find that inter-cyclone variability in LWP in both
observations and models is strongly driven by the moisture flux along the
cyclone's warm conveyor belt (WCB). Stronger WCB moisture flux enhances the
LWP within cyclones. This relationship is replicated in GCMs, although its
strength varies substantially across models. It is found that more than
80 % of the enhancement in Southern Hemisphere (SH) extratropical cyclone LWP in GCMs in
response to a transient 4 K warming can be predicted based on the
relationship between the WCB moisture flux and cyclone LWP in the historical
climate and their change in moisture flux between the historical and warmed
climates. Further, it is found that that the robust trend in
cyclone LWP over the Southern Ocean in observations and GCMs is consistent with changes in the moisture flux. We propose two cloud feedbacks acting
within extratropical cyclones: a negative feedback driven by
ClausiusClapeyron increasing water vapor path (WVP), which enhances the
amount of water vapor available to be fluxed into the cyclone, and a feedback
moderated by changes in the life cycle and vorticity of cyclones under
warming, which changes the rate at which existing moisture is imported into
the cyclone. Both terms contribute to increasing LWP within the cyclone.
While changes in moisture flux predict cyclone LWP trends in the current
climate and the majority of changes in LWP in transient warming simulations,
a portion of the LWP increase in response to climate change that is
unexplained by increasing moisture fluxes may be due to phase transitions.
The variability in LWP within cyclone composites is examined to understand
what cyclonic regimes the mixed-phase cloud feedback is relevant to. At a
fixed WCB moisture flux cyclone LWP increases with increasing sea surface temperature (SST) in the half
of the composite poleward of the low and decreases in the half equatorward of
the low in both GCMs and observations. Cloud-top phase partitioning observed
by the Atmospheric Infrared Sounder (AIRS) indicates that phase transitions
may be driving increases in LWP in the poleward half of cyclones.

Research areas: Severe Storms
Antecedent Rainfall, Excessive Vegetation Growth and Its Relation to Wildfire Burned Areas in California (2021)

In recent years, California has experienced wildfire seasons of increasing frequency and magnitude, especially in terms of wildfire burned areas. Here, we investigate the precipitation and vegetation growth preceding each wildfire season for the 20012020 period to determine if antecedent rainfall led to enhanced vegetation growth in wildfire burned areas in California. Precipitation data from the Global Precipitation Measurement (GPM) Mission and Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) data are examined to determine if antecedent rainfall and vegetation were normal, above, or below average, for the peak wet (DecemberFebruary) and peak growth (MarchMay) periods preceding each wildfire season, as well as for the two previous wet and growth seasons. Then spatial correlations are done between precipitation and the vegetation indexes to determine the statistical significance of the relationships. Eleven of the 20 wildfire seasons were generally found to be preceded by above average levels of precipitation and vegetation growth. In three of the six seasons with more wildfire activities, linear regression model results show high positive correlations between precipitation and vegetation, suggesting that antecedent rainfall fueled vegetation growth that might have caused a significant impact on wildfire burned areas. Most of the larger wildfires in California, including the largest on record, exhibited positive correlations between antecedent rainfall and vegetation. Our results suggest that antecedent rainfall in California led to enhanced vegetation growth that later fueled more than half of the wildfire seasons analyzed in this study.

Research areas: Wildfires, Land Surface/Agriculture Indicators
Preliminary validation of high-detailed GSA/Resurs-P tropospheric NO2 maps with alternative satellite measurements and transport simulations (2019)

An experiment for the retrieval of the high-detailed spatial NO2 distribution in the troposphere using measurements of the GSA instrument onboard the Resurs-P satellite was performed in 2016. The authors developed an algorithm to obtain the tropospheric NO2 2D distribution with the horizontal spatial resolution reaching 2,4 km for the first time at the world level and provided on a grid with a step of 120 m. The high spatial resolution of the NO2 space measurements for the first time allowed the identification of local sources of NO2 pollution and their plumes. The paper presents preliminary results of validation of the GSA high-detailed NO2 field obtained on September 29, 2016 for Hebei province, the North China Plain, which is the most NO2 polluted area in the world. To validate the coarse structures in the obtained NO2 field we performed comparisons with OMI NO2 observations having the resolution of 13 km x 24 km. The comparison confirmed the reliability of the GSA NO2 fields in general. For the validation of fine structures detected in the NO2 fields of GSA/Resurs-P, we are developing methods based on comparisons with chemical transport models. The paper presents preliminary comparison of the Resurs-P tropospheric NO2 field with simulation based on HYSPLIT dispersion model. For the solution of the problem, a high-detailed chemical transport model is under the development.

Research areas: Air Quality
Likelihood of Transformation to Green Infrastructure Using Ensemble Machine Learning Techniques in Jinan, China (2022)

Rapid urbanization influences green infrastructure (GI) development in cities. The government plans to optimize GI in urban areas, which requires understanding GI spatiotemporal trends in urban areas and driving forces influencing their pattern. Traditional GIS-based methods, used to determine the greening potential of vacant land in urban areas, are incapable of predicting future scenarios based on the past trend. Therefore, we propose a heterogeneous ensemble technique to determine the spatial pattern of GI development in Jinan, China, based on driving biophysical and socioeconomic factors. Data-driven artificial neural networks (ANN) and random forests (RF) are selected as base learners, while support vector machine (SVM) is used as a meta classifier. Results showed that the stacking model ANN-RF-SVM achieved the best test accuracy (AUC 0.941) compared to the individual ANN, RF, and SVM algorithms. Land surface temperature, distance to water bodies, population density, and rainfall are found to be the most influencing factors regarding vacant land conversion to GI in Jinan.

Research areas: Land Surface/Agriculture Indicators, Habitat Conversion/Fragmentation