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NASA NTRS2017

FOREWORD: Satellite Remote Sensing Beyond 2015

Satellite remote sensing has progressed tremendously since the first Landsat was launched on June 23, 1972. Since the 1970s, satellite remote sensing and associated airborne and in situ measurements have resulted in vital and indispensable observations for understanding our planet through time. These observations have also led to dramatic improvements in numerical simulation models of the coupled atmosphere-land-ocean systems at increasing accuracies and predictive capability. The same observations document the Earth's climate and are driving the consensus that Homo sapiens is changing our climate through greenhouse gas emissions. These accomplishments are the combined work of many scientists from many countries and a dedicated cadre of engineers who build the instruments and satellites that collect Earth observation data from satellites, all working toward the goal of improving our understanding of the Earth. This edition of the Remote Sensing Handbook (Vol. I, II, and III) is a compendium of information for many research areas of our Planet that have contributed to our substantial progress since the 1970s. Remote sensing community is now using multiple sources of satellite and in situ data to advance our studies, what ever they might be. In the following paragraphs, I will illustrate how valuable and pivotal role satellite remote sensing has played in climate system study over last five decades, The Chapters in the Remote Sensing Handbook (Vol. I, II, and III) provides many other specific studies on land, water, and other applications using EO data of last five decades, The Landsat system of Earth-observing satellites has led the way in pioneering sustained observations of our planet. From 1972 to the present, at least one and sometimes two Landsat satellites have been in operation. Starting with the launch of the first NOAA-NASA Polar Orbiting Environmental Satellites NOAA-6 in 1978, improved imaging of land, clouds, and oceans and atmospheric soundings of temperature were accomplished. The NOAA system of polar-orbiting meteorological satellites has continued uninterrupted since that time, providing vital observations for numerical weather prediction. These same satellites are also responsible for the remarkable records of sea surface temperature and land vegetation index from the Advanced Very High Resolution Radiometers (AVHRR) that now span more than 33 years, although no one anticipated these valuable climate records from this instrument before the launch of NOAA-7 in 1981. The success of data from the AVHRR led to the design of the MODIS instruments on NASA's Earth Observing System of satellite platforms that improved substantially upon the AVHRR. The first of the EOS platforms, Terra, was launched in 2000 and the second of these platforms, Aqua, was launched in 2002.

Tucker, Compton J.↗

NASA NTRS2018

Climatespark: an In-Memory Distributed Computing Framework for Big Climate Data Analytics

The unprecedented growth of climate data creates new opportunities for climate studies, and yet big climate data pose a grand challenge to climatologists to efficiently manage and analyze big data. The complexity of climate data content and analytical algorithms increases the difficulty of implementing algorithms on high performance computing systems. This paper proposes an in-memory, distributed computing framework, ClimateSpark, to facilitate complex big data analytics and time-consuming computational tasks. Chunking data structure improves parallel I/O efficiency, while a spatiotemporal index is built for the chunks to avoid unnecessary data reading and preprocessing. An integrated, multi-dimensional, array-based data model (ClimateRDD) and ETL operations are developed to address big climate data variety by integrating the processing components of the climate data lifecycle. ClimateSpark utilizes Spark SQL and Apache Zeppelin to develop a web portal to facilitate the interaction among climatologists, climate data, analytic operations and computing resources (e.g., using SQL query and Scala/Python notebook). Experimental results show that ClimateSpark conducts different spatiotemporal data queries/analytics with high efficiency and data locality. ClimateSpark is easily adaptable to other big multiple- dimensional, array-based datasets in various geoscience domains.

Hu, Fei↗

NASA NTRSDate not supplied

Evaluating Changes in Freshwater Health in the Tonlé Sap Basin using Remotely Sensed Data

The Tonlé Sap lake and river basin provide freshwater resources to about 4.5 million people in central Cambodia. Accelerating dam construction, intensifying agriculture, deforestation, and changing climate patterns threaten the health of the lake and the food security of nearby communities. NASA DEVELOP collaborated with Conservation International to calculate multiple components of the ecosystem vitality indicator of the Freshwater Health Index for Tonlé Sap lake from 2000 – 2020 using remote sensing. Landcover classifications, lake level altimetry, groundwater storage and nutrient flows were derived from multiple satellites including Landsat 5, Landsat 7, Landsat 8, Sentinel-2, the Global Precipitation Measurement (GPM)mission, and the Gravity Recovery and Climate Experiment (GRACE). Additionally, the Soil and Water Assessment Tool (SWAT) was used to analyze nutrient flow and water quality in the basin. A breakdown in the volume and regularity of annual lake levels from 2000-2020 was observed, reflecting increased pressure on water supply and agricultural productivity. A decrease in landcover naturalness was also observed and at least 8% of forested areas in the basin were lost while rice harvest intensity increased throughout the study period. Additionally, an initial water quality analysis was demonstrated using SWAT. Continued collaboration with Conservation International and local representatives will guide the remaining water quality analyses and allow them to make informed decisions regarding freshwater management and resource allocation in the Tonlé Sap lake and river basin.

Adriana Le Compte↗

NASA NTRS2020

Modelling climate change impacts on maize yields under low nitrogen input conditions in sub‐Saharan Africa

Smallholder farmers in sub‐Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low‐input systems is currently lacking. We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi‐arid Rwanda, hot subhumid Ghana and hot semi‐arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in‐season soil water content from 2‐year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall. Without N fertilizer input, maize (i) benefited less from an increase in atmospheric [CO2], (ii) was less affected by higher temperature or decreasing rainfall and (iii) was more affected by increased rainfall because N leaching was more critical. The model inter-comparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low-input systems. Climate change and N input interactions have strong implications for the design of robust adaptation practices across SSA, because the impact of climate change will be modified if farmers intensify maize production with more mineral fertilizer.

Crop simulation model↗

NASA NTRS2017

Changing Seasonality of Panarctic Tundra Vegetation in Relationship to Climatic Variables

Potential climate drivers of Arctic tundra vegetation productivity are investigated to understand recent greening and browning trends documented by maximum normalized difference vegetation index (NDVI) (MaxNDVI) and time-integrated NDVI (TI-NDVI) for 19822015. Over this period, summer sea ice has continued to decline while oceanic heat content has increased. The increases in summer warmth index (SWI) and NDVI have not been uniform over the satellite record. SWI increased from 1982 to the mid-1990s and remained relatively flat from 1998 onwards until a recent upturn. While MaxNDVI displays positive trends from 19822015, TI-NDVI increased from 1982 until 2001 and has declined since. The data for the first and second halves of the record were analyzed and compared spatially for changing trends with a focus on the growing season. Negative trends for MaxNDVI and TI-NDVI were more common during 19992015 compared to 19821998.

sea ice↗

NASA NTRSDate not supplied

Haiti Agriculture II: Evaluating the Success of Reforestation Practices in Haiti

The Caribbean country of Haiti has an extensive history of deforestation and environmental degradation stemming from French colonization. Over the past 33 years, the Haiti Reforestation Partnership (HRP) and their partners, Comprehensive Development Program (CODEP), have planted approximately 15.52 million trees. However, these efforts lacked scientific guidance to ensure successful forest stand survival. The NASA DEVELOP team partnered with the HRP to create a habitat suitability model (HSM) by using PlanetScope and Sentinel-2 Multispectral Instrument (MSI) imagery. The team also incorporated Landsat 8 and 9 OLI surface temperature, Centre National de L’Information Geo-Spatiale (CNIGS) Airborne Lidar, and ancillary datasets to analyze areas suitable for future reforestation efforts. The habitat model suggested locations with higher forest stand survival based on topography, soil health, climate, and feasibility to access suggested locations. We confirmed the HSM through a cross-analyzation of high enhanced vegetation index (EVI) values. Areas with lower EVI values and higher suitability based on the HSM were suggested for future planting as they lack well-established forest stands but have optimal conditions for growth. Through the creation of the HSM, the team provided the HRP with static maps of high suitability, a 3D printed elevation model, and a guidebook for animators. Additionally, the team provided a structured video highlighting the HRP’s efforts. Effective reforestation and better forest stand survival would help to achieve the goal of securing community food security. This would also serve as a guide to expand planting efforts into other locations and communities.

Kelli Roberts↗

NASA NTRS2008

Climate Change Implications to Vegetation Production in Alaska

Investigation of long-term meteorological satellite data revealed statistically significant vegetation response to climate drivers of temperature, precipitation and solar radiation with exclusion of fire disturbance in Alaska. Abiotic trends were correlated to satellite remote sensing observations of normalized difference vegetation index to understand biophysical processes that could impact ecosystem carbon storage. Warming resulted in disparate trajectories for vegetation growth due to precipitation and photosynthetically active radiation variation. Interior spruce forest low lands in late summer through winter had precipitation deficit which resulted in extensive fire disturbance and browning of undisturbed vegetation with reduced post-fire recovery while Northern slope moist alpine tundra had increased production due to warmer-wetter conditions during the late 1990s and early 2000s. Coupled investigation of Alaska s vegetation response to warming climate found spatially dynamic abiotic processes with vegetation browning not a result from increased fire disturbance.

Neigh, Christopher S.R.↗

NASA NTRS2013

Estimating the Mean Annual Surface Air Temperature at Armagh Observatory, Northern Ireland, and the Global Land-Ocean Temperature Index for Sunspot Cycle 24, the Current Ongoing Sunspot Cycle

As noted by Gray et al., Sir William Herschel was the first to suggest a possible close connection between the Sun and the Earth’s climate. The Sun, being the source of energy that impacts and drives the Earth’s climate system, displays a variety of changes over both short and long term time scales, the most obvious examples being the somewhat regular waxing and waning of sunspots with time (i.e., the sunspot cycle (SC)), first described by Samuel Heinrich Schwabe, a German apothecary and amateur astronomer who observed the Sun from Dessau, Germany, and the now well established variation of the Sun’s irradiance over the SC. Other factors related to the SC have been linked to changes in climate as well. Some of these other factors include the role of cosmic rays and the solar wind (i.e., the geomagnetic cycle) on climate, as well as the apparent close association between trends in global and northern hemispheric temperature and the length of the SC, although some investigators have described the inferred association between climate and, in particular, SC length as now being weak. More recently, Solheim et al. have reported on the relation between SC length and the average temperature in the same and immediately following SC for a number of meteorological stations in Norway and in the North Atlantic region. They noted that while they found no significant trend (correlation) between SC length and the average temperature when measured for the same cycle, in contrast, they found a significant negative trend when SC length was compared with the following cycle’s average temperature. From this observation, they suggested that average northern hemispheric temperature during the present ongoing SC (SC24) will be lower by about 0.9 °C than was seen in SC23 (spanning 1996–2007, based on yearly averages of sunspot number (SSN), and onset for SC24 occurring in 2008). The purpose of this Technical Publication (TP) is to examine the annual variations of the Armagh surface air temperature (ASAT) and the Global Land-Ocean Temperature Index (GLOTI) in relation to SSN and the SC in order to determine their likely values during SC24. Hence, it may provide insight as to whether solar forcing of global temperature is now lessening as a contributor to global warming, thereby indicating a possible cooling in the near term immediate future that potentially could ameliorate the effect of increased anthropogenic warming.

Wilson, Robert M.↗

NASA NTRS2021

Relationship between circum-Arctic atmospheric wave patterns and large-scale wildfires in boreal summer

Long-term assessment of severe wildfires and associated air pollution and related climate patterns in and around the Arctic is essential for assessing healthy human life status. To examine the relationships, we analyzed the National Aeronautics and Space Administration (NASA) modern-era retrospective analysis for research and applications, version 2 (MERRA-2). Our investigation based on this state-of-the-art atmospheric reanalysis data reveals that 13 out of the 20 months with the highest PM2.5 (corresponding to the highly elevated organic carbon in the particulate organic matter [POM] form) monthly mean mass concentration over the Arctic for 2003–2017 were all in summer (July and August), during which POM of ⩾0.5 μg/cu. m and PM2.5 were positively correlated. This correlation suggests that high PM2.5 in the Arctic is linked to large wildfire contributions and characterized by significant anticyclonic anomalies (i.e. clockwise atmospheric circulation) with anomalous surface warmth and drier conditions over Siberia and subpolar North America, in addition to Europe. A similar climate pattern was also identified through an independent regression analysis for the July and August mean data between the same atmospheric variables and the sign-reversed Scandinavian pattern index. We named this pattern of recent atmospheric circulation anomalies the circum-Arctic wave (CAW) pattern as a manifestation of eastward group-velocity propagation of stationary Rossby waves (i.e. large-scale atmospheric waves). The CAW induces concomitant development of warm anticyclonic anomalies over Europe, Siberia, Alaska, and Canada, as observed in late June 2019. Surprisingly, the extended regression analysis of the 1980–2017 period revealed that the CAW pattern was not prominent before 2003. Understanding the CAW pattern under future climate change and global warming would lead to better prediction of co-occurrences of European heatwaves and large-scale wildfires with air pollution over Siberia, Alaska, and Canada in and around the Arctic in summer.

wildfire↗

NASA NTRS2013

Linking Climate to Incidence of Zoonotic Cutaneous Leishmaniasis (L. major) in Pre-Saharan North Africa

Shifts in surface climate may have changed the dynamic of zoonotic cutaneous leishmaniasis (ZCL) in the pre-Saharan zones of North Africa. Caused by Leishmania major, this form multiplies in the body of rodents serving as reservoirs of the disease. The parasite is then transmitted to human hosts by the bite of a Phlebotomine sand fly (Diptera: Psychodidae) that was previously fed by biting an infected reservoir. We examine the seasonal and interannual dynamics of the incidence of this ZCL as a function of surface climate indicators in two regions covering a large area of the semi-arid Pre-Saharan North Africa. Results suggest that in this area, changes in climate may have initiated a trophic cascade that resulted in an increase in ZCL incidence. We find the correlation between the rainy season precipitation and the same year Normalized Difference Vegetation Index (NDVI) to be strong for both regions while the number of cases of ZCL incidence lags the precipitation and NDVI by 2 years. The zoonotic cutaneous leishmaniasis seasonal dynamic appears to be controlled by minimum temperatures and presents a 2-month lag between the reported infection date and the presumed date when the infection actually occurred. The decadal increase in the number of ZCL occurrence in the region suggests that changes in climate increased minimum temperatures sufficiently and created conditions suitable for endemicity that did not previously exist. We also find that temperatures above a critical range suppress ZCL incidence by limiting the vector's reproductive activity.

NDIV↗

NASA NTRS2019

Exploiting the Convergence of Evidence in Satellite Data for Advanced Weather Index Insurance Design

The goal of drought-related weather index insurance (WII) is to protect smallholder farmers against the risk of weather shocks and to increase their agricultural productivity. Estimates of precipitation and vegetation greenness are the two dominant satellite datasets. However, ignoring additional moisture- and energy-related processes that influence the response of vegetation to rainfall leads to an incomplete representation of the hydrologic cycle. This study evaluates the added value of considering multiple independent satellite-based variables to design, calibrate, and validate weather insurance indices on the African continent. The satellite data include two rainfall datasets, soil moisture, the evaporative stress index (ESI), and vegetation greenness. We limit artificial advantages by resampling all datasets to the same spatial (0.25°) and temporal (monthly) resolution, although datasets with a higher spatial resolution might have an added value, if considered as the single source of information for localized applications. A higher correlation coefficient between the moisture-focused variables and the normalized difference vegetation index (NDVI), an indicator for vegetation vigor, provides evidence for the datasets’ capability to capture agricultural drought conditions on the ground. The Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) rainfall dataset, soil moisture, and ESI show higher correlations with the (lagged) NDVI in large parts of Africa, for different land covers and various climate zones, than the African Rainfall Climatology, version 2 (ARC2), rainfall dataset, which is often used in WII. A comparison to drought years as reported by farmers in Ethiopia, Senegal, and Zambia indicates a high “hit rate” of all satellite-derived anomalies regarding the detection of severe droughts but limitations regarding moderate drought events.

drought↗

NASA NTRS2021

Climate Drivers of Arctic Tundra Variability and Change Using an Indicators Framework

This study applies an indicators framework to investigate climate drivers of tundra vegetation trends and variability over the 1982–2019 period. Previously known indicators relevant for tundra productivity (summer warmth index (SWI), coastal spring sea-ice (SI) area, coastal summer open-water (OW)) and three additional indicators (continentality, summer precipitation, and the Arctic Dipole (AD): second mode of sea level pressure variability) are analyzed with maximum annual Normalized Difference Vegetation Index (MaxNDVI) and the sum of summer bi-weekly (time-integrated) NDVI (TI-NDVI) from the Advanced Very High Resolution Radiometer time-series. Climatological mean, trends, and correlations between variables are presented. Changes in SI continue to drive variations in the other indicators. As spring SI has decreased, summer OW, summer warmth, MaxNDVI, and TI-NDVI have increased. However, the initial very strong upward trends in previous studies for MaxNDVI and TI-NDVI are weakening and becoming spatially and temporally more variable as the ice retreats from the coastal areas. TI-NDVI has declined over the last decade particularly over High Arctic regions and southwest Alaska. The continentality index (CI) (maximum minus minimum monthly temperatures) is decreasing across the tundra, more so over North America than Eurasia. The relationship has weakened between SI and SWI and TI-NDVI, as the maritime influence of OW has increased along with total precipitation. The winter AD is correlated in Eurasia with spring SI, summer OW, MaxNDVI, TI-NDVI, the CI and total summer precipitation. This winter connection to tundra emphasizes the role of SI in driving the summer indicators. The winter (DJF) AD drives SI variations which in turn shape summer OW, the atmospheric SWI and NDVI anomalies. The winter and spring indicators represent potential predictors of tundra vegetation productivity a season or two in advance of the growing season.

Uma S. Bhatt↗

NASA NTRS2023

Quantifying the Importance of Selected Drought Indicators for the United States Drought Monitor

Using information theory, our study quantifies the importance of selected indicators for the U.S. Drought Monitor (USDM) maps. We use the technique of mutual information (MI) to measure the importance of any indicator to the USDM, and because MI is derived solely from the data, our findings are independent of any model structure (conceptual, physically-based, or empirical). We also compare these MIs against the drought representation effectiveness ratings in the North America Drought Indices and Indicators Assessment (NADIIA) survey for Koeppen climate zones. This reveals: [1] agreement between some ratings and our MI values (high for example indicators like Standardized Precipitation-Evapotranspiration Index or SPEI); [2] some divergences (for example, soil moisture has high ratings but near-zero MIs for ESA-CCI soil moisture in the Western U.S., indicating the need of another remotely sensed soil moisture source); and [3] new insights into the importance of variables such as Snow Water Equivalent (SWE) that are not included in sources like NADIIA. Further analysis of the MI results yields findings related to: [1] hydrological mechanisms (summertime SWE domination during individual drought events through snowmelt into the water-scarce soil); [2] hydroclimatic types (the top pair of inputs in the Western and non-Western regions are SPEIs and soil moistures respectively); and [3] predictability (high for the California 2012-2017 event, with longer-timescale indicators dominating). Finally, the high MIs between multiple indicators jointly and the USDM indicate potentially high drought forecasting accuracies achievable using only model-based inputs, and the potential for global drought monitoring using only remotely sensed inputs, especially for locations having insufficient in situ observations.

Drought↗

NASA NTRS2002

Near-Real-Time Detection and Monitoring of Dust Events by Satellite (SeaWIFS, MODIS, and TOMS)

Over the last few years satellites have given us increasingly detailed information on the size, location, and duration of dust events around the world. These data not only provide valuable feedback to the modelling community as to the fidelity of their aerosol models but are also finding increasing use in near real-time applications. In particular, the ability to locate and track the development of aerosol dust clouds on a near real-time basis is being used by scientists and government to provide warning of air pollution episodes over major urban area. This ability has also become a crucial component of recent coordinated campaigns to study the characteristics of tropospheric aerosols such as dust and their effect on climate. One such recent campaign was ACE-Asia, which was designed to obtain the comprehensive set of ground, aircraft, and satellite data necessary to provide a detailed understanding of atmospheric aerosol particles over the Asian-Pacific region. As part of ACE-Asia, we developed a near real-time data processing and access system to provide satellite data from the polar-orbiting instruments Earth Probe TOMS (in the form of absorbing aerosol index) and SeaWiFS (in the form of aerosol optical thickness, AOT, and Angstrom exponent). The results were available via web access. The location and movement information provided by these data were used both in support of the day-to-day flight planning of ACE-Asia and as input into aerosol transport models. While near real-time SeaWiFS data processing can be performed using either the normal global data product or data obtained via direct broadcast to receiving stations close to the area of interest, near real-time MODIS processing of data to provide aerosol retrievals is currently only available using its direct broadcast capability. In this paper, we will briefly discuss the algorithms used to generate these data. The retrieved aerosol optical thickness and Angstrom exponent from SeaWiFS will be compared with those obtained from various AERONET sites over the Asian-Pacific region. The TOMS aerosol index will also be compared with AERONET aerosol optical thickness over different aerosol conditions, and comparisons between the MODIS and SeaWiFS data will also be presented. Finally, we will discuss the climate implication of our studies using the combined satellite and AERONET observations.

Hsu, N. Christina↗

NASA NTRS2019

Climates of Warm Earth-Like Planets III: Fractional Habitability from a Water Cycle Perspective

The habitable fraction of a planet's surface is important for the detectability of surface biosignatures. The extent and distribution of habitable areas are influenced by external parameters that control the planet's climate, atmospheric circulation, and hydrological cycle. We explore these issues using the ROCKE-3D general circulation model, focusing on terrestrial water fluxes and thus the potential for the existence of complex life on land. Habitability is examined as a function of insolation and planet rotation for an Earth-like world with zero obliquity and eccentricity orbiting the Sun. We assess fractional habitability using an aridity index that measures the net supply of water to the land. Earth-like planets become "superhabitable" (a larger habitable surface area than Earth) as insolation and day-length increase because their climates become more equable, reminiscent of past warm periods on Earth when complex life was abundant and widespread. The most slowly rotating, most highly irradiated planets, though, occupy a hydrological regime unlike any on Earth, with extremely warm, humid conditions at high latitudes but little rain and subsurface water storage. Clouds increasingly obscure the surface as insolation increases, but visibility improves for modest increases in rotation period. Thus, moderately slowly rotating rocky planets with insolation near or somewhat greater than modern Earth's appear to be promising targets for surface characterization by a future direct imaging mission.

Del Genio, Anthony D.↗

NASA NTRS2019

Climates of Warm Earth-like Planets. III. Fractional Habitability from a Water Cycle Perspective

Exoplanet atmospheric variability↗

NASA NTRS2022

Inferring iron-oxide species content in atmospheric mineral dust from DSCOVR EPIC observations

The iron-oxide content of dust in the atmosphere and most notably its apportionment between hematite (α-Fe2O3) and goethite (α-FeOOH) are key determinants in quantifying dust’s light absorption, its top of atmosphere UV radiances used for dust monitoring, and ultimately shortwave dust direct radiative effects (DRE). Hematite and goethite column mass concentrations and iron-oxide mass fractions of total dust mass concentration were retrieved from the DeepSpace Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) measurements in the ultraviolet–visible (UV–Vis) channels. The retrievals were performed for dust-identified aerosol plumes over land using aerosol optical depth (AOD) and spectral imaginary refractive index provided by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm over six continental regions (North America, North Africa, West Asia, Central Asia, EastAsia, and Australia). The dust particles are represented as an internal mixture of non-absorbing host and absorbing hematite and goethite. We use the Maxwell–Garnett effective medium approximation with carefully selected complex refractive indices of hematite and goethite that produce mass fractions of iron oxides species consistent with in situ values found in the literature to derive the hematite and goethite volumetric/mass concentrations from MAIAC EPIC products. We compared the retrieved hematite and goethite concentrations with in situ dust aerosol mineralogical content measurements, as well as with published data. Our data display variations within the published range of hematite, goethite, and iron-oxide mass fractions for pure mineral dust cases. A specific analysis is presented for 15 sites over the main dust source regions. Sites in the central Sahara, Sahel, and Middle East exhibit a greater temporal variability of iron oxides relative to other sites. Niger site(13.52°N, 2.63°E) is dominated by goethite over Harmattan season with median of ~2 weight percentage (wt.%) of iron-oxide. Saudi Arabia site (27.49°N, 41.98°E) over Middle East also exhibited surge of goethite content with the beginning of Shamal season. The Sahel dust is richer in iron-oxide than Saharan and northern China dust except in Summer. The Bodélé Depression area shows a distinctively lower iron-oxide concentration (~1 wt.%) throughout the year. Finally, we show that EPIC data allow to constrain the hematite refractive index. Specifically, we select 5 out of 13 different number of hematite refractive indices widely variable in published laboratory studies by constraining the iron-oxide mass ratio to the known measured values. Provided climatology of hematite and goethite mass fractions across main dust regions of the Earth will be useful for dust shortwave DRE studies and climate modeling.

Sujung Go↗

NASA NTRS2017

Principal Modes of Precipitation Variability from Preliminary Series of IMERG Data

The Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) mission, IMERG, is the unified U.S. algorithm that provides merged Microwave Infrared (IR) satellite precipitation product for the U.S. GPM team. Even though IMERG record is still very short, 2014-2016, it is tempting to test if it captures ENSO and NAO signals as compared to the popular, still on-going, TRMM Multi-satellite Precipitation Analysis, TMPA. El Nino Southern Oscillation (ENSO) is the most significant mode of interannual variability of tropical ocean atmosphere. North Atlantic Oscillation (NAO) impact is on monthly scales and is mostly an atmospheric mode in the North Atlantic. There exist well-defined, multivariate, indexes that represent ENSO and NAO conditions and phase.

climate variability↗