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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2013

Impact Assessment of Biomass Burning on Air Quality in Southeast and East Asia During BASE-ASIA

A synergy of numerical simulation, ground-based measurement and satellite observation was applied to evaluate the impact of biomass burning originating from Southeast Asia (SE Asia) within the framework of NASA's 2006 Biomass burning Aerosols in Southeast Asia: Smoke Impact Assessment (BASE-ASIA). Biomass burning emissions in the spring of 2006 peaked in MarcheApril when most intense biomass burning occurred in Myanmar, northern Thailand, Laos, and parts of Vietnam and Cambodia. Model performances were reasonably validated by comparing to both satellite and ground-based observations despite overestimation or underestimation occurring in specific regions due to high uncertainties of biomass burning emission. Chemical tracers of particulate K(+), OC concentrations, and OC/EC ratios showed distinct regional characteristics, suggesting biomass burning and local emission dominated the aerosol chemistry. CMAQ modeled aerosol chemical components were underestimated at most circumstances and the converted AOD values from CMAQ were biased low at about a factor of 2, probably due to the underestimation of biomass emissions. Scenario simulation indicated that the impact of biomass burning to the downwind regions spread over a large area via the Asian spring monsoon, which included Southern China, South China Sea, and Taiwan Strait. Comparison of AERONET aerosol optical properties with simulation at multi-sites clearly demonstrated the biomass burning impact via longrange transport. In the source region, the contribution from biomass burning to AOD was estimated to be over 56%. While in the downwind regions, the contribution was still significant within the range of 26%-62%.

biomass burning↗
NASA NTRS2023

A Regional Map of Mangrove Extent for Myanmar, Thailand, and Cambodia Shows Losses of 44% By 1996

Southeast Asia is home to some of the planet’s most carbon-dense and biodiverse mangrove ecosystems. There is still much uncertainty with regards to the timing and magnitude of changes in mangrove cover over the past 50 years. While there are several regional to global maps of mangrove extent in Southeast Asia over the past two decades, data prior to the mid-1990s is limited due to the scarcity of Earth Observation data of sufficient quality and the historical limitations to publicly available EO. We present a classification of mangrove extent using Landsat 1-2 MSS Tier 2 data from 1972-1977 for three Southeast Asian countries: Myanmar, Thailand, and Cambodia. Mangrove extent land cover maps were generated using a Random Forest machine learning algorithm that effectively mapped a total of 15,420.51 km2. Accuracy assessments indicated that the classification for the mangrove and non-mangrove class had a producer’s accuracy of 79.63 % and 98.24 %a user’s accuracy of 89.58 % and 96.21 %, and an overall accuracy of 95.26 % . We found a decline of 6,830 km2 between the 1970’s and 2020, showing that44.29% of the mangrove area in these countries has been lost in the past 48 years. Most of this loss occurred between the 1970’s and 1996; rates of deforestation declined dramatically after 1996. This study also urges that these results be considered within the context of the local need for these coastal resources that helped them endure the political and financial challenges of their coastal homes.

Priscilla Baltezar↗
NASA NTRS2003

Transport Pathways for Asian Pollution Outflow Over the Pacific: Interannual and Seasonal Variations

The meteorological pathways contributing to Asian pollution outflow over the Pacific are examined with a global three-dimensional model analysis of CO observations from the Transport and Chemical Evolution over the Pacific (TRACE-P) aircraft mission (February-April 2001). The model is used also to place the TRACE-P observations in an interannual (1994-2001) and seasonal context. The major process driving Asian pollution outflow in spring is frontal lifting ahead of southeastward-moving cold fronts (the leading edge of cold surges) and transport in the boundary layer behind the cold fronts. Orographic lifting over central and eastern China combines with the cold fronts to promote the transport of Chinese pollution to the free troposphere. Outflow of seasonal biomass burning in Southeast Asia during spring takes place mostly by deep convection but also by northeastward transport and frontal lifting, mixing with the anthropogenic outflow. Boundary layer outflow over the western Pacific is largely devoid of biomass burning influence. European and African (biomass burning) plumes in Asian outflow during TRACE-P were weak (less than 60 ppbv and 20 ppbv CO, respectively) and were not detectable in the observations because of superposition of the much larger Asian pollution signal. Spring 2001 (La Nina) was characterized by unusually frequent cold surge events in the Asian Pacific rim and strong convection in Southeast Asia, leading to unusually strong boundary layer outflow of anthropogenic emissions and convective outflow of biomass burning emissions in the upper troposphere. The Asian outflow flux of CO to the Pacific is found to vary seasonally by a factor of 3-4 (maximum in March and minimum in summer). The March maximum results from frequent cold surge events and seasonal biomass burning emissions.

Liu, Hong-Yu↗
NASA NTRS2002

Mesoscale Convective Systems During SCSMEX: Simulations with a Regional Climate Model and a Cloud-Resolving Model

The South China Sea Monsoon Experiment (SCSMEX) was conducted in May-June 1998. One of its major objectives is to better understand the key physical processes for the onset and evolution of the summer monsoon over Southeast Asia and southern China (Lau et al. 2000). Multiple observation platforms (e.g., soundings, Doppler radar, ships, wind seafarers, radiometers, etc.) during SCSMEX provided a first attempt at investigating the detailed characteristics of convection and circulation changes, associated with monsoons over the South China Sea region. SCSMEX also provided precipitation derived from atmospheric budgets (Johnson and Ciesielski 2002) and comparison to those obtained from the Tropical Rainfall Measuring Mission (TRMM). In this paper, a regional climate model and a cloud-resolving model are used to perform multi-day integrations to understand the precipitation processes associated with the summer monsoon over Southeast Asia and southern China. The regional climate model is used to understand the soil - precipitation interaction and feedback associated with a flood event that occurred in and around China's Atlantic River during SCSMEX. Sensitivity tests on various land surface models, cumulus parameterization schemes (CASE), sea surface temperature (SST) variations and midlatitude influences are also performed to understand the processes associated with the onset of the monsoon over the S. China Sea during SCSMEX. Cloud-resolving models (CRMs) use more sophisticated and physically realistic parameterizations of cloud microphysical processes with very fine spatial and temporal resolution. One of the major characteristics of CRMs is an explicit interaction between clouds, radiation and the land/ocean surface. It is for this reason that GEWEX (Global Energy and Water Cycle Experiment) has formed the GCSS (GEWEX Cloud System Study) expressly for the purpose of improving the representation of the moist processes in large-scale models using CRMs. The Goddard Cumulus Ensemble (GCE) model is a CRM and is used to simulate convective systems associated with the onset of the South China Sea monsoon in 1998. The BRUCE model includes the same land surface model, cloud physics, and radiation scheme used in the regional climate model. A comparison between the results from the GCE model and regional climate model is performed.

Tao, W. K.↗
NASA NTRS2007

Modeling Malaria Transmission in Thailand and Indonesia

Malaria Modeling and Surveillance is a project in the NASA Applied Sciences Public Health Applications Program. The main objectives of this project are: 1) identification of the potential breeding sites for major vector species: 2) implementation of a malaria transmission model to identify they key factors that sustain or intensify malaria transmission; and 3) implementation of a risk algorithm to predict the occurrence of malaria and its transmission intensity. Remote sensing and GIs are the essential elements of this project. The NASA Earth science data sets used in this project include AVHRR Pathfinder, TRMM, MODIS, NSIPP and SIESIP. Textural-contextual classifications are used to identify small larval habitats. Neural network methods are used to model malaria cases as a function of precipitation, temperatures, humidity and vegetation. Hindcastings based on these environmental parameters have shown good agreement to epidemiological records. Examples for spatio-temporal modeling of malaria transmissions in Southeast Asia are given. Discrete event simulations were used for modeling the detailed interactions among the vector life cycle, sporogonic cycle and human infection cycle, under the explicit influences of selected extrinsic and intrinsic factors. The output of the model includes the individual infection status and the quantities normally observed in field studies, such as mosquito biting rates, sporozoite infection rates, gametocyte prevalence and incidence. Results are in good agreement with mosquito vector and human malaria data acquired by Coleman et al. over 4.5 years in Kong Mong Tha, a remote village in western Thailand. Application of our models is not restricted to Southeast Asia. The model and techniques are equally applicable to other regions of the world, when appropriate epidemiological and vector ecological parameters are used as input.

Kiang, Richard↗
NASA NTRS2019

Global Disease Outbreaks Associated with the 2015-2016 El Niño Event

Interannual climate variability patterns associated with the El Niño-Southern Oscillation phenomenon result in climate and environmental anomaly conditions in specific regions worldwide that directly favor outbreaks and/or amplification of variety of diseases of public health concern including chikungunya, hantavirus, Rift Valley fever, cholera, plague, and Zika. We analyzed patterns of some disease outbreaks during the strong 2015–2016 El Niño event in relation to climate anomalies derived from satellite measurements. Disease outbreaks in multiple El Niño-connected regions worldwide (including Southeast Asia, Tanzania, western US, and Brazil) followed shifts in rainfall, temperature, and vegetation in which both drought and flooding occurred in excess (14–81% precipitation departures from normal). These shifts favored ecological conditions appropriate for pathogens and their vectors to emerge and propagate clusters of diseases activity in these regions. Our analysis indicates that intensity of disease activity in some ENSO-teleconnected regions were approximately 2.5–28% higher during years with El Niño events than those without. Plague in Colorado and New Mexico as well as cholera in Tanzania were significantly associated with above normal rainfall (p < 0.05); while dengue in Brazil and southeast Asia were significantly associated with above normal land surface temperature (p < 0.05). Routine and ongoing global satellite monitoring of key climate variable anomalies calibrated to specific regions could identify regions at risk for emergence and propagation of disease vectors. Such information can provide sufficient lead-time for outbreak prevention and potentially reduce the burden and spread of ecologically coupled diseases.

El Nino↗
NASA NTRS2002

Mesoscale Convective Systems During SCSMEX: Simulations with a Regional Climate Model and a Cloud-Resolving Model

The South China Sea Monsoon Experiment (SCSMEX) was conducted in May-June 1998. One of its major objectives is to better understand the key physical processes for the onset and evolution of the summer monsoon over Southeast Asia and southern China. Multiple observation platforms (e.g., upper-air soundings, Doppler radar, ships, wind profilers, radiometers, etc.) during SCSMEX provided a first attempt at investigating the detailed characteristics of convection and circulation changes associated with monsoons over the South China Sea region. SCSMEX also provided precipitation derived from atmospheric budgets and comparison to those obtained from the Tropical Rainfall Measuring Mission (TRMM). In this paper, a regional scale model (with grid size of 20 km) and Goddard Cumulus Ensemble (GCE) model (with 1 km grid size) are used to perform multi-day integration to understand the precipitation processes associated with the summer monsoon over Southeast Asia and southern China. The regional climate model is used to understand the soil-precipitation interaction and feedback associated with a flood event that occurred in and around China's Yantz River during SCSMEX Sensitivity tests on various land surface models, sea surface temperature (SST) variations, and cloud processes are performed to understand the precipitation processes associated with the onset of the monsoon over the S. China Sea during SCSMEX. These tests have indicated that the land surface model has a major impact on the circulation over the S. China Sea. Cloud processes can effect the precipitation pattern while SST variation can effect the precipitation amounts over both land and ocean. The exact location (region) of the flooding can be effected by the soil-rainfall feedback. The GCE-model results captured many observed precipitation characteristics because it used a fine grid size. For example, the model simulated rainfall temporal variation compared quite well to the sounding-estimated rainfall. The results show there are more latent heat fluxes prior to the onset of the monsoon. However, more rainfall was simulated after the onset of the monsoon. This modeling study indicates the latent heat fluxes (or evaporation) have more of an impact on precipitation processes and rainfall in the regional climate model simulations than in the cloud-resolving model simulations. Research is underway to determine if the difference in the grid sizes or the moist processes used in these two models is responsible for the differing influence of surface fluxes an precipitation processes.

Tao, W.-K.↗
NASA NTRS2021

Developing a Hydrological Monitoring and Sub-Seasonal to Seasonal Forecasting System for South and Southeast Asian River Basins

South and Southeast Asia is subject to significant hydrometeorological extremes, including drought. Under rising temperatures, growing populations, and an apparent weakening of the South Asian monsoon in recent decades, concerns regarding drought and its potential impacts on water and food security are on the rise. Reliable sub-seasonal to seasonal (S2S) hydrological forecasts could, in principle, help governments and international organizations to better assess risk and act in the face of an oncoming drought. Here, we leverage recent improvements in S2S meteorological forecasts and the growing power of Earth Observations to provide more accurate monitoring of hydrological states for forecast initialization. Information from both sources is merged in a South and Southeast Asia sub-seasonal to seasonal hydrological forecasting system (SAHFS-S2S), developed collaboratively with the NASA SERVIR program and end-users across the region. This system applies the Noah-MultiParameterization (NoahMP) Land Surface Model (LSM) in the NASA Land Information System (LIS), driven by downscaled meteorological fields from the Global Data Assimilation System (GDAS) and Climate Hazards InfraRed Precipitation products (CHIRP and CHIRPS) to optimize initial conditions. The NASA Goddard Earth Observing System Model - sub-seasonal to seasonal (GEOS-S2S) forecasts, downscaled using the National Center for Atmospheric Research (NCAR) General Analog Regression Downscaling (GARD) tool and quantile mapping, are then applied to drive 5-km resolution hydrological forecasts to a 9-month forecast time horizon. Results show that the skillful predictions of root zone soil moisture can be made one to two months in advance for forecasts initialized in rainy seasons and up to 8 months when initialized in dry seasons. The memory of accurate initial conditions can positively contribute to forecast skills throughout the entire 9-month prediction period in areas with limited precipitation. This SAHFS-S2S has been operationalized at the International Centre for Integrated Mountain Development (ICIMOD) to support drought monitoring and warning needs in the region.

Yifan Zhou↗
NASA NTRS2003

TRMM Fire Algorithm, Product and Applications

Land fires are frequent menaces to human lives and property. They also change the state of the vegetation and contribute to the climate forcing by releasing large amount of aerosols and greenhouse gases into the atmosphere. This paper summarizes methodologies of detecting global land fires from the Tropical Rainfall Measuring Mission (TRMM) Visible Infrared Scanner FIRS) measurements. The TRMM Science Data and Information System (TSDIS) fire products include global images of daily hot spots and monthly fire counts at 0.5 deg. x 0.5 deg. resolution, as well as text fiies that details necessary information of all fire pixels. The information includes date, orbit number, pixel number, local time, solar zenith angle, latitude, longitude, reflectance of visible/near infrared channels, brightness temperatures of infrared channels, as well as background brightness temperatures of infrared channels. These products have been archived since January 1998. The TSDIS fire products are compared with the coincidental European Commission (EC) Joint Research Center (JRC) 1 km AVHRR fire products. Analyses of the TSDIS monthly fire products during the period from 1998 to 2003 manifested seasonal cycles of biomass fires over Southeast Asia, Africa, North America and South America. The data also showed interannual variations associated with the 98/99 ENS0 cycle in Central America and the Indonesian region. In order to understand the variability of global land fires and their effects on the distribution of atmospheric aerosols, statistical methods were applied to the TSDIS fire products as well as to the Total Ozone Mapping Spectrometer (TOMS) aerosol index products for a period of five years from January 1998 to December 2002. The variability of global atmospheric aerosol is consistent with the fire variations over these regions during this period. The correlation between fire count and TOMS aerosol index is about 0.55 for fire pixels in Southeast Asia, Indonesia, and Africa. Parallel statistical analyses such as Empirical Orthogonal Function (EOF) analysis and Singular Spectrum Analysis (SSA) methods were applied to pentad TRMM fire data and TOMS aerosol data. The EOF analyses showed contrast between North and South hemispheres and also inter- continental transitions in Africa and America. EOF and SSA analyses also identified 25-60 day intra-seasonal oscillations that were superimposed on the annual cycles of both fire and aerosol data. The intra-seasonal variability of fires showed similarity of tropical rainfall oscillation modes. The TRMM fire products were also compared to the coincident TRMh4 rainfall and other rainfall products to investigate the interaction between rainfall and fire. The results indicate that the annual, interannual and intraseasonal variability of fire are dominated by global rainfall variations. However, the feedback of fire to the rainfall occurrence at regional scale for certain regions is also evident.

Ji, Yi-Min↗
NASA NTRS2020

Influence of Synoptic-Dynamic Meteorology on the Long-Range Transport of Indochina Biomass Burning Aerosols

Impacts of long-range transported biomass burning aerosols from northern Indochina on regional air quality, atmospheric visibility, and climate effects have been discussed extensively in the literature over the past decade. However, the mechanism between aerosol transport and the low-level jet (LLJ) in synoptic meteorology dynamics (e.g., the frontal system) is still not fully understood. In this study, we present results of aerosol-LLJ interaction based on the National Aeronautics and Space Administration/Modern Era Retrospective-analysis for Research and Application version 2 (NASA/MERRA-2) dataset simultaneous to field campaigns in spring 2013–2015 over Southeast Asia. For linking the atmospheric circulation to aerosol transport, a general meteorological condition favorable for aerosol transport is demonstrated. Biomass burning aerosol accumulation over the source region (i.e., northern Indochina) coincided with weak westerly system over the northern South China Sea, and as the synoptic weather system evolved, the aerosols were transported to downwind regions (i.e., Taiwan and West Pacific) by a cold front and LLJ dynamics. The MERRA-2 reanalysis data show the postcold front accompanied a LLJ stream at approximately 700 hPa (3 km) and accelerated the haze plume transport eastward with a life cycle of approximately 3–5 days, an event that reoccurred multiple times from March to April. Our results indicate that coexistence of LLJ and a South China Sea anticyclone at 700 hPa, which primarily could drive the mechanism behind these transport events. Furthermore, more observational data from Southeast Asia would greatly benefit studies of aerosol-meteorology interactions in the region.

Hsiang-Yu Huang↗
NASA NTRSDate not supplied

Capacity Building Engagement for Estimating Forest Stand Height Utilizing the SAR Handbook

Utilizing Synthetic Aperture Radar (SAR) which provides information such as vegetation structure and moisture content, independently of weather conditions is a powerful tool that can be employed to more effectively monitor deforestation and estimate biomass which is of paramount concern, especially in South and Southeast Asia which contain nearly 15% of all tropical forests. SERVIR, a NASA-USAID initiative, works in partnership with regional hubs around the world building capacity through promoting the use of Earth observations and geospatial technologies. The SERVIR Global network and the Silva Carbon program collaboratively developed the SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation which set out to reduce the barriers associated with leveraging SAR and increase the capacity of researchers and practitioners globally to use SAR for monitoring. In particular, utilizing SAR to estimate Forest Stand Height (FSH) is crucial for greenhouse gas emissions reporting and estimating carbon storage. Programs such as Reducing Emissions from Deforestation and Forest Degradation (REDD) rely on parameters such as these for their monitoring efforts. This talk will showcase the collaborative efforts, emergent geospatial technology leveraged, and capacity building approaches utilized by our team to conduct a South and Southeast Asia focused in-person training event located at SERVIR-Mekong, a SERVIR hub lead by the Asian Disaster Preparedness Center (ADPC) in Bangkok.

AGU↗
NASA NTRS2015

Spatial and Temporal Analysis of a Global Landslide Catalog

Landslide inventories are critical to support investigations ofwhere andwhen landslides have happened andmay occur in the future; however, there is surprisingly little information on the historical occurrence of landslides at the global scale. This paper presents a new publicly available global landslide catalog (GLC), which is based on media reports, online databases, and other sources. This database is currently available at http://ojo-streamer. herokuapp.com/. The 5741 points in the GLC provide a foundation for evaluating spatial and temporal trends in landslide activity from 2007 to 2013. Globally, landslideswere reportedmost frequently from July to September. Most events occurred in Asia, North America and Southeast Asia. In contrast, fewer than 5% of the fatalities were reported in North America, suggesting a significant amount of under-reporting in other regions as well as potential discrepancies between developing and developed regions. Reported landslide events were also compared to satellite-based precipitation estimates fromthe Tropical RainfallMeasuring Mission (TRMM) to evaluate the co-occurrence of extreme precipitation and landslide activity. Of the 3550 points considered in a subset of the GLC, approximately 60% of the reported landslides have daily precipitation exceeding the 95th percentile of precipitation calculated over a 14-year TRMMrecord for the same location. This study also investigated how the recurrence interval of extreme precipitation corresponded to some of the most catastrophic landslide events. In spite of several reporting and cataloging biases, spatial and temporal analysis of the GLC suggests that it is a valuable database for characterizing global patterns of landslide occurrence and evaluating relationships with extreme precipitation at regional and global scales.

Analysis↗
NASA NTRS2012

Analysis of Vegetation Index Variations and the Asian Monsoon Climate

Vegetation growth depends on local climate. Significant anthropogenic land cover and land use change activities over Asia have changed vegetation distribution as well. On the other hand, vegetation is one of the important land surface variables that influence the Asian Monsoon variability through controlling atmospheric energy and water vapor conditions. In this presentation, the mean and variations of vegetation index of last decade at regional scale resolution (5km and higher) from MODIS have been analyzed. Results indicate that the vegetation index has been reduced significantly during last decade over fast urbanization areas in east China, such as Yangtze River Delta, where local surface temperatures were increased significantly in term of urban heat Island. The relationship between vegetation Index and climate (surface temperature, precipitation) over a grassland in northern Asia and over a woody savannas in southeast Asia are studied. In supporting Monsoon Asian Integrated Regional Study (MAIRS) program, the data in this study have been integrated into Giovanni, the online visualization and analysis system at NASA GES DISC. Most images in this presentation are generated from Giovanni system.

Shen, Sunhung↗
NASA NTRS2020

Determination of Lidar Ratio for Major Aerosol Types over Western North Pacific Based on Long-Term MPLNET Data

East Asia is the most complex region in the world for aerosol studies, as it encounters a lot of varieties of aerosols, and aerosol classification can be a challenge in this region. In the present study, we focused on the relationship between aerosol types and aerosol optical properties. We analyzed the long-term (2005–2012) data of vertical profiles of aerosol extinction coefficients, lidar ratio (Sp), and other aerosol optical properties obtained from a NASA Micro-Pulse Lidar Network and Aerosol Robotic Network site in northern Taiwan, which frequently receives Asian continental outflows. Based on aerosol extinction vertical profiles, the profiles were classified into two types: type 1 (single-layer structure) and type 2 (two-layer structure). Fall season (October–November) was the prevailing season for the Type 1, whereas type 2 mainly happened in spring (March–April). In type 1, air masses normally originated from three regional sectors, i.e., Asia continental (AC), Pacific Ocean (PO), and Southeast Asia (SA). The mean Sp values were 39 ± 17 sr, 30 ± 12 sr, and 38 ± 18 sr for the AC, PO, and SA sectors, respectively. The Sp results suggested that aerosols from the AC sector contained dust and anthropogenic particles, and aerosols from the PO sector were most likely sea salts. We further combined the EPA dust event database and backward trajectory analysis for type 2. Results showed that Sp was 41 ± 14 sr and 53 ± 21 sr for dust storm and biomass-burning events, respectively. The Sp for biomass-burning events in type 2 showed two peaks patterns. The first peak occurred within range of 30–50 sr corresponding to urban pollutant, and the second peak occurred within range of 60–80 sr in relation to biomass burning. Finally, our study summarized the Sp values for four major aerosol types over northern Taiwan, viz., urban (42 ± 18 sr), dust (34 ± 6 sr), biomass-burning (69 ± 12 sr), and oceanic (30 ± 12 sr). Our findings provide useful references for aerosol classification and air pollution identification over the western North Pacific.

Sheng-Hsiang Wang↗
NASA NTRS2023

Fine particulate concentrations over East Asia derived from aerosols measured by the Advanced Himawari Imager using machine learning

Fine particulate matter with a diameter below 2.5 μm (PM 2.5 ) is deleterious to the cardiovascular and respiratory systems. It is often difficult to assess the effects of PM 2.5 on human health over regions with limited ground monitoring sites, especially in East Asia. As an alternative, we estimated near-surface PM 2.5 concentrations by analyzing Advanced Himawari Imager (AHI) Yonsei Aerosol Retrieval (YAER) products. This study incorporates daytime data for East Asia covering the Korean Peninsula, China, Japan, Southeast Asia, and southern Mongolia. We collocated AHI YAER product pixels with meteorological, land-cover, and other ancillary data for the period from March 2018 to February 2019. To estimate PM 2.5 concentrations over wide areas spanning many countries displaying various relationships between aerosol optical depth and PM 2.5 , monthly models were developed by considering both the spatial and temporal characteristics of ground-based PM 2.5 measurements. Random forest machine learning model estimated ground-level mass concentrations of PM 2.5 ; subsequent 10-fold cross validation (CV) yielded a CV R 2 value of 0.81 and a CV root mean squared error (RMSE) of 12.3 μg m -3 . We investigated the spatial pattern of PM 2.5 concentrations over multiple countries and seasonal variation in PM 2.5 concentrations. Diurnal variation of a severe PM 2.5 event in the Korean Peninsula was investigated as a case study. The model captured the extremely heterogeneous spatial distribution of PM 2.5 concentrations peaked around local noon. To measure the capability of the developed model to estimate PM 2.5 concentrations in areas with few in-situ data, its predictive performance was evaluated using a dataset independent of the training process with an R 2 of 0.60 and RMSE of 8.18 μg m −3 . This study demonstrates the potential for satellite-based PM 2.5 estimation for areas with insufficient measuring stations.

Pm2.5↗
NASA NTRS2016

SEACIONS During the 2012 Asian Monsoon: A Strategic Approach to Determining Convective Impacts on Tropospheric Ozone and TTL Gravity Waves

Purpose of making ozone and water vapor profiles measurements in SEAC4RS is to give consistent coverage of the vertical structure at fixed sites to (1) complement 2 campaign aircraft sampling; (2) ground-truth satellite measurements of H O and ozone; (3) provide profiles for model evaluation; (4) study processes responsible for day-to-day variability at each site. Revised objective for 2013, due to cancellation of the 2012 and 2013 plans to operate in Southeast Asia: rapidly re-configure the original "SEACIONS," Southeast Asian Consortium for Intensive Ozonesonde Network Study, to a SouthEast American plan (SEACIONS) for collecting daily ozonesonde data during DC-8 and ER-2 flights throughout the southeastern US. As in previous IONS (2004, 2006, 2008), students were trained at St Louis, Tallahassee, Houston, Penn State, Huntsville, Socorro. Images of the soundings and related flight-planning products were posted each day at NASA and Penn State (PSU) websites. With the aircraft based at Ellington Field (Houston), water CFH (cryogenic frost-point hygrometer) sondes in addition to ozonesondes, were to be taken launched at that site.

Thompson, Anne M.↗
NASA NTRS2024

Irrigation-Driven Groundwater Depletion in the Ganges-Brahmaputra Basin Decreases the Streamflow in the Bay of Bengal

Ganges and Brahmaputra, two of Asia’s most prominent rivers, have a crucial role in Southeast Asia’s geopolitics and economy and are home to one of the world’s biggest marine ecosystems. Irrigation-driven groundwater depletion and climate change affect the Ganges-Brahmaputra’s hydrology, threatening the stability of the Bay of Bengal. Here, we quantify, using results from a land reanalysis, the impacts of a changing climate and intensive irrigation on the surface water flowing into the Bay of Bengal. The effects of such activities mostly occurring in the Ganges basin, either intensified or lessened depending on the area by the climatic conditions, decrease freshwater flow into the bay by up to 1200 m 3 /s/year. While the increase in precipitation in the Ganges basin reduces the effects of groundwater depletion on the streamflow, the decrease in precipitation and the snowmelt decline in the Brahmaputra basin exacerbate streamflow reduction due to groundwater depletion at the delta.

Climate change↗
NASA NTRS2017

The South/Southeast Asia Research Initiative (SARI) Update and Meeting Objectives

Land Use/Cover Change (LU/CC) is one of the most important types of environmental change in South and Southeast Asian countries. Several studies suggest that LU/CC in these countries is in large part driven by population growth and economic development. In the region, changes that are most common include urban expansion, agricultural land loss, land abandonment, deforestation, logging, reforestation, etc. To address the research needs and priorities in the region, a regional initiative entitled South Southeast Asia Regional Initiative (SARI) has been developed involving US and regional scientists. The initiative is funded by NASA Land Cover, Land Use Change program. The goal of SARI is to integrate state-of-the-art remote sensing, natural sciences, engineering and social sciences to enrich LU/CC science in South Southeast Asian countries. In the presentation, LU/CC change research in SARI countries will be highlighted including the drivers of change. For example, in South Asia, forest cover has been increasing in countries like India, Nepal and Bhutan due to sustainable afforestation measures; whereas, large-scale deforestation in Southeast Asian countries is still continuing, due to oil palm plantation expansion driven by the international market demand in Malaysia and Indonesia. With respect to urbanization, South and Southeast Asian countries contain 23 megacities, each with more than 10 million people. Rapid urbanization is driving agricultural land loss and agricultural intensification has been increasing due to less availability of land for growing food crops such as in India, Vietnam, and Thailand. The drivers of LUCC vary widely in the region and include such factors as land tenure, local economic development, government policies, inappropriate land management, land speculation, improved road networks, etc. In addition, variability in the weather, climate, and socioeconomic factors also drive LU/CC resulting in disruptions of biogeochemical cycles, radiation and the surface energy balance of the atmosphere. The presentation will also highlight SARI collaborative activities with space agencies, universities and non-government organizations including data sharing mechanisms in the region.

Research↗