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At least 577 records · Page 32
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NASA NTRS2013

Decadal Prediction Skill in the GEOS-5 Forecast System

A suite of decadal predictions has been conducted with the NASA Global Modeling and Assimilation Office's (GMAO's) GEOS-5 Atmosphere-Ocean general circulation model. The hind casts are initialized every December 1st from 1959 to 2010, following the CMIP5 experimental protocol for decadal predictions. The initial conditions are from a multivariate ensemble optimal interpolation ocean and sea-ice reanalysis, and from GMAO's atmospheric reanalysis, the modern-era retrospective analysis for research and applications. The mean forecast skill of a three-member-ensemble is compared to that of an experiment without initialization but also forced with observed greenhouse gases. The results show that initialization increases the forecast skill of North Atlantic sea surface temperature compared to the uninitialized runs, with the increase in skill maintained for almost a decade over the subtropical and mid-latitude Atlantic. On the other hand, the initialization reduces the skill in predicting the warming trend over some regions outside the Atlantic. The annual-mean Atlantic meridional overturning circulation index, which is defined here as the maximum of the zonally-integrated overturning stream function at mid-latitude, is predictable up to a 4-year lead time, consistent with the predictable signal in upper ocean heat content over the North Atlantic. While the 6- to 9-year forecast skill measured by mean squared skill score shows 50 percent improvement in the upper ocean heat content over the subtropical and mid-latitude Atlantic, prediction skill is relatively low in the sub-polar gyre. This low skill is due in part to features in the spatial pattern of the dominant simulated decadal mode in upper ocean heat content over this region that differ from observations. An analysis of the large-scale temperature budget shows that this is the result of a model bias, implying that realistic simulation of the climatological fields is crucial for skillful decadal forecasts.

Decadal Prediction↗
NASA NTRS2015

An Assessment of Southern Ocean Water Masses and Sea Ice During 1988-2007 in a Suite of Interannual CORE-II Simulations

We characterise the representation of the Southern Ocean water mass structure and sea ice within a suite of 15 global ocean-ice models run with the Coordinated Ocean-ice Reference Experiment Phase II (CORE-II) protocol. The main focus is the representation of the present (1988-2007) mode and intermediate waters, thus framing an analysis of winter and summer mixed layer depths; temperature, salinity, and potential vorticity structure; and temporal variability of sea ice distributions. We also consider the interannual variability over the same 20 year period. Comparisons are made between models as well as to observation-based analyses where available. The CORE-II models exhibit several biases relative to Southern Ocean observations, including an underestimation of the model mean mixed layer depths of mode and intermediate water masses in March (associated with greater ocean surface heat gain), and an overestimation in September (associated with greater high latitude ocean heat loss and a more northward winter sea-ice extent). In addition, the models have cold and fresh/warm and salty water column biases centred near 50 deg S. Over the 1988-2007 period, the CORE-II models consistently simulate spatially variable trends in sea-ice concentration, surface freshwater fluxes, mixed layer depths, and 200-700 m ocean heat content. In particular, sea-ice coverage around most of the Antarctic continental shelf is reduced, leading to a cooling and freshening of the near surface waters. The shoaling of the mixed layer is associated with increased surface buoyancy gain, except in the Pacific where sea ice is also influential. The models are in disagreement, despite the common CORE-II atmospheric state, in their spatial pattern of the 20-year trends in the mixed layer depth and sea-ice.

Sea ice↗
NASA NTRS2015

Late-Glacial to Late-holocene Shifts in Global Precipitation Delta(sup 18)O

Reconstructions of Quaternary climate are often based on the isotopic content of paleo-precipitation preserved in proxy records. While many paleo-precipitation isotope records are available, few studies have synthesized these dispersed records to explore spatial patterns of late-glacial precipitation delta(sup 18)O. Here we present a synthesis of 86 globally distributed groundwater (n 59), cave calcite (n 15) and ice core (n 12) isotope records spanning the late-glacial (defined as 50,000 to 20,000 years ago) to the late-Holocene (within the past 5000 years). We show that precipitation delta(sup 18)O changes from the late-glacial to the late-Holocene range from -7.1% (delta(sup 18)O(late-Holocene) > delta(sup 18)O(late-glacial) to +1.7% (delta(sup 18)O(late-glacial) > delta(sup 18)O(late-Holocene), with the majority (77) of records having lower late-glacial delta(sup 18)O than late-Holocene delta(sup 18)O values. High-magnitude, negative precipitation delta(sup 18)O shifts are common at high latitudes, high altitudes and continental interiors.

isotopes↗
NASA NTRS2015

Extension and Statistical Analysis of the GACP Aerosol Optical Thickness Record.

The primary product of the Global Aerosol Climatology Project (GACP) is a continuous record of the aerosol optical thickness (AOT) over the oceans. It is based on channel-1 and -2 radiance data from the Advanced Very High Resolution Radiometer (AVHRR) instruments flown on successive National Oceanic and Atmospheric Administration (NOAA) platforms. We extend the previous GACP dataset by four years through the end of 2009 using NOAA-17 and -18 AVHRR radiances recalibrated against MODerate resolution Imaging Spectroradiometer (MODIS) radiance data, thereby making the GACP record almost three decades long. The temporal overlap of over three years of the new NOAA-17 and the previous NOAA-16 record reveals an excellent agreement of the corresponding global monthly mean AOT values, thereby confirming the robustness of the vicarious radiance calibration used in the original GACP product. The temporal overlap of the NOAA-17 and -18 instruments is used to introduce a small additive adjustment to the channel-2 calibration of the latter resulting in a consistent record with increased data density. The Principal Component Analysis (PCA) of the newly extended GACP record shows that most of the volcanic AOT variability can be isolated into one mode responsible for ~12% of the total variance. This conclusion is confirmed by a combined PCA analysis of the GACP, MODIS, andMulti-angle Imaging SpectroRadiometer (MISR) AOTs during the volcano-free period fromFebruary 2000 to December 2009.We show that the modes responsible for the tropospheric AOT variability in the three datasets agree well in terms of correlation and spatial patterns. A previously identified negative AOT trend which started in the late 1980s and continued into the early 2000s is confirmed. Its magnitude and duration indicate that it was caused by changes in tropospheric aerosols. The latest multi-satellite segment of the GACP record shows that this trend tapered off, with no noticeable AOT change after 2002. This result is consistent with the MODIS andMISR AOT records as well as with the recent gradual reversal frombrightening to dimming revealed by surface flux measurements in many aerosol producing regions. Thus the robustness of the GACP record is confirmed, increasing our confidence in the validity of the negative trend. Although the nominal negative GACP AOT trend could partially be an artifact of increasing aerosol absorption, we argue that the time dependence of the GACP record, including the latest flat period, is more consistent with the actual decrease in the tropospheric AOT.

aerosols↗
NASA NTRS2015

Comparison of GFED3, QFED2 and FEER1 Biomass Burning Emissions Datasets in a Global Model

Biomass burning contributes about 40% of the global loading of carbonaceous aerosols, significantly affecting air quality and the climate system by modulating solar radiation and cloud properties. However, fire emissions are poorly constrained in models on global and regional levels. In this study, we investigate 3 global biomass burning emission datasets in NASA GEOS5, namely: (1) GFEDv3.1 (Global Fire Emissions Database version 3.1); (2) QFEDv2.4 (Quick Fire Emissions Dataset version 2.4); (3) FEERv1 (Fire Energetics and Emissions Research version 1.0). The simulated aerosol optical depth (AOD), absorption AOD (AAOD), angstrom exponent and surface concentrations of aerosol plumes dominated by fire emissions are evaluated and compared to MODIS, OMI, AERONET, and IMPROVE data over different regions. In general, the spatial patterns of biomass burning emissions from these inventories are similar, although the strength of the emissions can be noticeably different. The emissions estimates from QFED are generally larger than those of FEER, which are in turn larger than those of GFED. AOD simulated with all these 3 databases are lower than the corresponding observations in Southern Africa and South America, two of the major biomass burning regions in the world.

biomass burning↗
NASA NTRS2016

Comparison of AIRS Version-6 OLR Climatologies and Anomaly Time Series with Those of CERES and MERRA-2

RCs of AIRS and MERRA-2 500 mb specific humidity agree very well in terms of spatial patterns, but MERRA-2 ARCs are larger in magnitude and show a spurious moistening globally and over Central Africa. AIRS and MERRA-2 fractional cloud cover ARCs agree less well with each other. MERRA-2 shows a spurious global mean increase in cloud cover that is not found in AIRS, including a large spurious cloud increase in Central Africa. AIRS and MERRA-2 ARCs of surface skin and surface air temperatures are all similar to each other in patterns. AIRS shows a small global warming over the 13 year period, while MERRA-2 shows a small global cooling. This difference results primarily from spurious MERRA-2 temperature trends at high latitudes and over Central Africa. These differences all contribute to the spurious negative global MERRA-2 OLR trend. AIRS Version-6 confirms that 2015 is the warmest year on record and that the Earth's surface is continuing to warm.

AIRS↗
NASA NTRS2016

Have GRACE Satellites Overestimated Groundwater Depletion in the Northwest India Aquifer?

The Northwest India Aquifer (NWIA) has been shown to have the highest groundwater depletion (GWD) rate globally, threatening crop production and sustainability of groundwater resources. Gravity Recovery and Climate Experiment (GRACE) satellites have been emerging as a powerful tool to evaluate GWD with ancillary data. Accurate GWD estimation is, however, challenging because of uncertainties in GRACE data processing. We evaluated GWD rates over the NWIA using a variety of approaches, including newly developed constrained forward modeling resulting in a GWD rate of 3.1 plus or minus 0.1 centimeters per acre (or 14 plus or minus 0.4 cubic kilometers per acre) for Jan 2005-Dec 2010, consistent with the GWD rate (2.8 centimeters per acre or 12.3 cubic kilometers per acre) from groundwater-level monitoring data. Published studies (e.g., 4 plus or minus 1 centimeter per acre or 18 plus or minus 4.4 cubic kilometers per acre) may overestimate GWD over this region. This study highlights uncertainties in GWD estimates and the importance of incorporating a priori information to refine spatial patterns of GRACE signals that could be more useful in groundwater resource management and need to be paid more attention in future studies.

Long, Di↗
NASA NTRS2015

Climate-Induced Mortality of Spruce Stands in Belarus

The aim of this work is an analysis of the causes of spruce (Picea abies L.) decline and mortality in Belarus. The analysis was based on forest inventory and Landsat satellite (land cover classification, climate variables (air temperature, precipitation, evaporation, vapor pressure deficit, SPEI drought index)), and GRACE-derived soil moisture estimation (equivalent of water thickness anomalies, EWTA). We found a difference in spatial patterns between dead stands and all stands (i.e., before mortality). Dead stands were located preferentially on relief features with higher water stress risk (i.e., higher elevations, steeper slopes, south and southwestern exposure). Spruce mortality followed a series of repeated droughts between 1990 and 2010. Mortality was negatively correlated with air humidity (r = -0.52), and precipitation (r = -0.57), and positively correlated with the prior year vapor pressure deficit (r = 0.47), and drought increase (r = 0.57). Mortality increased with the increase in occurrence of spring frosts (r = 0.5), and decreased with an increase in winter cloud cover (r = -0.37). Spruce mortality was negatively correlated with snow water accumulation (r = -0.81) and previous year anomalies in water soil content (r = -0.8). Weakened by water stress, spruce stands were attacked by pests and phytopathogens. Overall, spruce mortality in Belarussian forests was caused by drought episodes and drought increase in synergy with pest and phytopathogen attacks. Vast Picea abies mortality in Belarus and adjacent areas of Russia and Eastern Europe is a result of low adaptation of that species to increased drought. This indicates the necessity of spruce replacement by drought-tolerant indigenous (e.g., Pinus sylvestris, Querqus robur) or introduced (e.g., Larix sp. or Pseudotsuga menzieslii) species to obtain sustainable forest growth management.

spruce decline↗
NASA NTRS2016

Combining Livestock Production Information in a Process-Based Vegetation Model to Reconstruct the History of Grassland Management

Grassland management type (grazed or mown) and intensity (intensive or extensive) play a crucial role in the greenhouse gas balance and surface energy budget of this biome, both at field scale and at large spatial scale. However, global gridded historical information on grassland management intensity is not available. Combining modelled grass-biomass productivity with statistics of the grass-biomass demand by livestock, we reconstruct gridded maps of grassland management intensity from 1901 to 2012. These maps include the minimum area of managed vs. maximum area of unmanaged grasslands and the fraction of mown vs. grazed area at a resolution of 0.5deg by 0.5deg. The grass-biomass demand is derived from a livestock dataset for 2000, extended to cover the period 19012012. The grass-biomass supply (i.e. forage grass from mown grassland and biomass grazed) is simulated by the process-based model ORCHIDEE-GM driven by historical climate change, risingCO2 concentration, and changes in nitrogen fertilization. The global area of managed grassland obtained in this study increases from 6.1 x 10(exp 6) km(exp 2) in 1901 to 12.3 x 10(exp 6) kmI(exp 2) in 2000, although the expansion pathway varies between different regions. ORCHIDEE-GM also simulated augmentation in global mean productivity and herbage-use efficiency over managed grassland during the 20th century, indicating a general intensification of grassland management at global scale but with regional differences. The gridded grassland management intensity maps are model dependent because they depend on modelled productivity. Thus specific attention was given to the evaluation of modelled productivity against a series of observations from site-level net primary productivity (NPP) measurements to two global satellite products of gross primary productivity (GPP) (MODIS-GPP and SIF data). Generally, ORCHIDEE-GM captures the spatial pattern, seasonal cycle, and inter-annual variability of grassland productivity at global scale well and thus is appropriate for global applications presented here.

Livestock dataset↗
NASA NTRS2017

NASA Earth Observations Informing Energy Management Decision Making

The Energy Sector is experiencing increasing impacts from severe weather and shifting climatic trends, as well as facing a changing political climate, adding uncertainty for stakeholders as they make short- and long-term planning investments. Climate changes such as prolonged extreme heat and drought (leading to wildfire spread, for example), sea level rise, and extreme storms are changing the ways that utilities operate. Energy infrastructure located in coastal or flood-prone areas faces inundation risks, such as damage to energy facilities. The use of renewable energy resources is increasing, requiring more information about their intermittency and spatial patterns. In light of these challenges, public and private stakeholders have collaborated to identify potential data sources, tools, and programmatic ideas. For example, utilities across the country are using cutting-edge technology and data to plan for and adapt to these changes. In the Federal Government, NASA has invested in preliminary work to identify needs and opportunities for satellite data in energy sector application, and the Department of Energy has similarly brought together stakeholders to understand the landscape of climate vulnerability and resilience for utilities and others. However, have these efforts improved community-scale resilience and adaptation efforts? Further, some communities are more vulnerable to climate change and infrastructure impacts than others. This session has two goals. First, panelists seek to share existing and ongoing efforts related to energy management. Second, the session seeks to engage with attendees via group knowledge exchange to connect national energy management efforts to local practice for increased community resilience.

Eckman, Richard↗
NASA NTRS2017

Blowing Snow Sublimation and Transport over Antarctica from 11 Years of CALIPSO Observations

Blowing snow processes commonly occur over the earth's ice sheets when the 10 mile wind speed exceeds a threshold value. These processes play a key role in the sublimation and redistribution of snow thereby influencing the surface mass balance. Prior field studies and modeling results have shown the importance of blowing snow sublimation and transport on the surface mass budget and hydrological cycle of high-latitude regions. For the first time, we present continent-wide estimates of blowing snow sublimation and transport over Antarctica for the period 2006-2016 based on direct observation of blowing snow events. We use an improved version of the blowing snow detection algorithm developed for previous work that uses atmospheric backscatter measurements obtained from the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) lidar aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite. The blowing snow events identified by CALIPSO and meteorological fields from MERRA-2 are used to compute the blowing snow sublimation and transport rates. Our results show that maximum sublimation occurs along and slightly inland of the coastline. This is contrary to the observed maximum blowing snow frequency which occurs over the interior. The associated temperature and moisture reanalysis fields likely contribute to the spatial distribution of the maximum sublimation values. However, the spatial pattern of the sublimation rate over Antarctica is consistent with modeling studies and precipitation estimates. Overall, our results show that the 2006-2016 Antarctica average integrated blowing snow sublimation is about 393 +/- 196 Gt yr(exp -1), which is considerably larger than previous model-derived estimates. We find maximum blowing snow transport amount of 5 Mt km-1 yr(exp -1) over parts of East Antarctica and estimate that the average snow transport from continent to ocean is about 3.7 Gt yr(exp -1). These continent-wide estimates are the first of their kind and can be used to help model and constrain the surface mass budget over Antarctica.

Antarctica Sublimation Ice-sheet↗
NASA NTRS2018

Tropical Cyclone Intensity Estimation Using Deep Convolutional Neural Networks

Estimating tropical cyclone intensity by just using satellite image is a challenging problem. With successful application of the Dvorak technique for more than 30 years along with some modifications and improvements, it is still used worldwide for tropical cyclone intensity estimation. A number of semi-automated techniques have been derived using the original Dvorak technique. However, these techniques suffer from subjective bias as evident from the most recent estimations on October 10, 2017 at 1500 UTC for Tropical Storm Ophelia: The Dvorak intensity estimates ranged from T2.3/33 kt (Tropical Cyclone Number 2.3/33 knots) from UW-CIMSS (University of Wisconsin-Madison - Cooperative Institute for Meteorological Satellite Studies) to T3.0/45 kt from TAFB (the National Hurricane Center's Tropical Analysis and Forecast Branch) to T4.0/65 kt from SAB (NOAA/NESDIS Satellite Analysis Branch). In this particular case, two human experts at TAFB and SAB differed by 20 knots in their Dvorak analyses, and the automated version at the University of Wisconsin was 12 knots lower than either of them. The National Hurricane Center (NHC) estimates about 10-20 percent uncertainty in its post analysis when only satellite based estimates are available. The success of the Dvorak technique proves that spatial patterns in infrared (IR) imagery strongly relate to tropical cyclone intensity. This study aims to utilize deep learning, the current state of the art in pattern recognition and image recognition, to address the need for an automated and objective tropical cyclone intensity estimation. Deep learning is a multi-layer neural network consisting of several layers of simple computational units. It learns discriminative features without relying on a human expert to identify which features are important. Our study mainly focuses on convolutional neural network (CNN), a deep learning algorithm, to develop an objective tropical cyclone intensity estimation. CNN is a supervised learning algorithm requiring a large number of training data. Since the archives of intensity data and tropical cyclone centric satellite images is openly available for use, the training data is easily created by combining the two. Results, case studies, prototypes, and advantages of this approach will be discussed.

tropical cyclone intensity↗
NASA NTRS2017

Atmospheric Ozone Response to the Disrupted 2015-2016 Quasi-Biennial Oscillation

The quasi-biennial oscillation (QBO) - a quasi-periodic alternation between easterly and westerly zonal winds in the tropical stratosphere - is a main driver of inter-annual ozone variability in the stratosphere. During the late-2015 through 2016 time period, the QBO experienced a major disruption unlike any observed since wind measurements began in 1953. We examined the ozone response to this QBO disruption using profile ozone measurements from the Aura Microwave Limb Sounder (MLS) and Ozone Mapping and Profiler Suite Limb Profiler and total column measurements from the Solar Backscatter Ultraviolet (SBUV) Merged Ozone Data Set (MOD). Positive anomalies in stratospheric equatorial O3 developed between 50 and 30 hPa in May-September of 2016, and negative ozone anomalies were observed in the subtropics of both hemispheres. As a consequence of this QBO disruption, extratropical total ozone values during the spring-summer 2016 were at or near seasonal record lows over the more than 40 years of the total ozone record, resulting in an increase of surface UV index during northern hemisphere summer. We found very consistent responses in all considered ozone observations in terms of time, amplitude and spatial patterns. We will show the ozone changes associated with this disrupted QBO throughout the winter and spring 2017.

Kramarova, N. A.↗
NASA NTRS2018

Spatially-Explicit Monitoring of Crop Photosynthetic Capacity Through the Use of Space-Based Chlorophyll Fluorescence Data

Plant functional traits such as photosynthetic capacity are critical parameters for terrestrial biosphere models. However, their spatial and temporal characteristics are still poorly represented. In this study, we used satellite observations of sun-induced fluorescence (SIF) to estimate top-of-canopy photosynthetic capacity (maximum carboxylation rate, V(sub cmax) at a reference temperature of 25 deg C) for crops, which was in turn utilized to simulate regional gross primary production (GPP). We first estimate the key parameter, V(sub cmax), in the widely-used FvCB photosynthesis model using field measurements of CO2 and water fluxes during 2007-2012 at seven crop eddy covariance flux sites over the US Corn Belt. The results showed that satellite far-red SIF retrievals have a stronger link to V(sub cmax) at the seasonal scale (R(squared)=0.70 for C4 and R(squared)=0.63 for C3 crop) as compared with widely-used vegetation indices. We calibrate an empirical model linking V(sub cmax) with SIF that was used to estimate spatially and temporally varying crop V(sub cmax) for the US Corn Belt region. The resulting V(sub cmax) maps are used together with meteorological data from MERRA reanalysis data and vegetation structural parameters derived from the satellite- based spectral reflectance data to constrain the Soil-Canopy Observation of Photosynthesis and Energy (SCOPE) balance model in order to estimate regional crop GPP. Our results show a substantial improvement in the seasonal and spatial patterns of cropland GPP when compared with crop yield inventory data. The evaluation with tall tower atmospheric CO2 measurements further supports our estimation of spatiotemporal V(sub cmax) from space-borne SIF. Considering that SIF has a direct link to photosynthetic activity, our findings highlight the potential to infer regional V(sub cmax) using remotely sensed SIF data and to use this information for a better quantification of regional cropland carbon cycles.

Drought↗
NASA NTRS2018

Observation Error Specification for Near-Surface Soil Moisture Assimilation: Does It Matter?

The observation error variances assumed in the data assimilation (DA) of remotely sensed near-surface soil moisture observations are known to have unrealistic spatial patterns. The temporal variability in the errors is also typically neglected. Soil moisture errors cannot be directly calculated at large scales, and the globally distributed errors are unknown. We apply several approaches to estimating the observation errors (R matrix) for near-surface soil moisture DA experiments, to establish whether the assumed errors are important to the DA performance, and hence whether uncertainty in the observation error specification is a likely source of uncertainty in soil moisture DA output.

Draper, Clara↗
NASA NTRS2018

Mapping Pine Plantations in the Southeastern U.S. Using Structural, Spectral, and Temporal Remote Sensing Data

The southeastern U.S. produces the most industrial roundwood in the U.S. each year, largely from commercial pine plantations. The extent of plantation forests and management dynamics can be difficult to ascertain from periodic forest inventories, yet short-rotation tree plantations also present challenges for remote sensing. Here, we integrated spectral, temporal, and structural information from airborne and satellite platforms to distinguish pine plantations from natural forests and evaluate the contribution from planted forests to regional forest coverin the southeastern U.S. Within flight lines from NASA Goddard's Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager, lidar metrics of forest structure had the highest overall accuracy for pine plantations among single-source classifications (90%), but the combination of spectral and temporal metrics from Landsat generated comparable accuracy (91%). Combined structural, temporal, and spectral information from G-LiHT and Landsat had the highest accuracy for plantations (92%) and natural forests (88%). At a regional scale, classifications using Landsat spectral and temporal metrics had between 74 and 82% mean class accuracy for plantations.Regionally, plantations accounted for 28% of forest cover in the southeastern U.S., a result similar to plot-based estimates, albeit with greater spatial detail. Regional maps of plantation forests differed from existing map products, including the National Land Cover Database. Combining plantation extent in 2011 with Landsat based forest change data identified strong regional gradients in plantation dynamics since 1985, with distinct spatial patterns of rotation age (east-west) and plantation expansion (interior). Our analysis demonstrates the potential to improve the characterization of dynamic land cover classes, including economically important timber plantations, by integrating diverse remote sensing datasets. Critically, multi-source remote sensing provides an approach to leverage periodic forest inventory data for annual monitoring of managed forest landscapes.

Fagan, M. E.↗
NASA NTRS2019

Evaluating the Uncertainty of Terrestrial Water Budget Components over High Mountain Asia

This study explores the uncertainties in terrestrial water budget estimation over High Mountain Asia (HMA) using a suite of uncoupled land surface model (LSM) simulations. The uncertainty in the water balance components of precipitation (P), evapotranspiration (ET), runoff (R), and terrestrial water storage (TWS) is significantly impacted by the uncertainty in the driving meteorology, with precipitation being the most important boundary condition. Ten gridded precipitation datasets along with a mix of model-, satellite-, and gauge-based products, are evaluated first to assess their suitability for LSM simulations over HMA. The datasets are evaluated by quantifying the systematic and random errors of these products as well as the temporal consistency of their trends. Though the broader spatial patterns of precipitation are generally well captured by the datasets, they differ significantly in their means and trends. In general, precipitation datasets that incorporate information from gauges are found to have higher accuracy with low Root Mean Square Errors and high correlation coefficient values. An ensemble of LSM simulations with selected subset of precipitation products is then used to produce the mean annual fluxes and their uncertainty over HMA in P, ET, and R to be 2.11 ± 0.45, 1.26 ± 0.11, and 0.85 ± 0.36 mm per day, respectively. The mean annual estimates of the surface mass (water) balance components from this model ensemble are comparable to global estimates from prior studies. However, the uncertainty/spread of P, ET, and R is significantly larger than the corresponding estimates from global studies. A comparison of ET, snow cover fraction, and changes in TWS estimates against remote sensing-based references confirms the significant role of the input meteorology in influencing the water budget characterization over HMA and points to the need for improving meteorological inputs.

Terrestrial water budget↗
NASA NTRS2018

Multi-Sensor Integration of Vegetation Index Products for Long-Term Monitoring of Vegetation Dynamics: A Case Study from MODIS to VIIRS

Spectral vegetation index (VI) time series data from moderate resolution sensors, such as Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS), have widely been used to identify "hot spot" areas of vegetation changes and to characterize long-term trends of vegetation changes. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor series of the Joint Polar Satellite System (JPSS) program is slated to continue the highly calibrated data stream initiated with MODIS. The first VIIRS sensor has operationally been acquiring Earth-reflected radiation since March 2012. The second VIIRS sensor (NOAA-20) was launched in November 2017, which is to continue the measurements made by the first VIIRS sensor. In this study, we examined an integrated use of MODIS and VIIRS VI time series data on capturing vegetation dynamics in the Asia-Pacific region using their overlapped period of observations (2013-2017). Three VIs, the "top-of-canopy (TOC)" normalized difference vegetation index (NDVI), TOC enhanced vegetation index (EVI), and TOC two-band enhanced vegetation index (EVI2), were investigated. For all the three VIs, VIIRS VIs were systematically higher than the MODIS counterparts due mainly to their spectral bandpass differences. However, both VIIRS and MODIS VIs showed the comparable spatial patterns in their temporal variations. Empirical spectral corrections allowed to merge the two data streams, which slightly improved the temporal resolution of the VI temporal profiles. These results suggest the suitability of VIIRS data to extend and merge into the MODIS VI record for long-term vegetation dynamics studies.

MODIS↗