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Home » Near-real time data assimilation for land vegetation and carbon cycle using JPSS space-based observations and in-situ data

Near-real time data assimilation for land vegetation and carbon cycle using JPSS space-based observations and in-situ data

Satellite remote sensing has been providing observations of the terrestrial biosphere for decades.
For instance, the NDVI index since the 1980s using NOAA’s AVHRR, followed by NASA’s
MODIS, and more recently the JPSS VIIRS instrument onboard the Suomi NPP (SNPP) satellite
that provides a suite of high quality land-surface observations. There is an opportunity to
significantly enhance the utility of the JPSS observations by bringing models and other datasets
in a coherent and comprehensive framework. This proposal will apply a land-vegetation-carbon
Data Assimilation (DA) system to satellite and in-situ observations, with the aim of providing an
assimilated data-model ‘best-analysis’ monitoring product of vegetation and carbon cycle.

This system includes the data assimilation core Local Ensemble Transform Kalman Filter
(LETKF), an advanced DA system that has been successfully applied in a variety of atmosphere,
ocean, and land applications including at NCEP and ECMWF. This assimilator will be applied to
two land-vegetation-carbon models: (1) VEGAS, a UMD-led model widely used in carbon cycle
and dynamic vegetation community, (2) NCEP/EMC’s operational land model Noah with its
dynamic vegetation model version Noah-MP. Weighing observation errors vs. model errors, the
DA system gives an optimized estimate of the state variables such as vegetation biomass, soil
carbon, and leaf area index (LAI). Key model parameters such as light use efficiency (LUE),
respiration dependence on temperature Q10, and phenological cold/drought tolerance are treated
as augmented state variables to be assimilated side-by-side. Gross Primary Productivity (GPP)
and surface fluxes of heat, H2O and CO2 will also be a product of this assimilation.

Our assimilation will include two strands of effort: (1) a historical 37-year period using a
NESDIS/STAR dataset based on harmonized operational global NOAA/AVHRR and 7-year
SNPP/VIIRS data, (2) a shorter current near-real time (NRT) period using the JPSS EDR land
product from VIIRS. In the historical strand, we will build on several datasets/experiences with a
broader suite of datasets, including AVHRR/VIIRS vegetation indices (VIs) and phenology,
FLUXNET carbon and water fluxes across ~200 sites around the world, and the novel remotely
sensed Solar-Induced chlorophyll Fluorescence (SIF) from the Orbiting Carbon Observatory 2
(OCO-2). In the NRT monitoring product, we will focus on the VIIRS vegetation indices
NDVI/EVI and OCO-2 SIF. The historical strand will provide a longer and historical background
that is useful for understanding interannual-decadal variability, as well as validate the system and
establish the best methodology, while the latter will provide an NRT monitoring product of
vegetation health, ecosystem dynamics, fire, agriculture and the global carbon cycle.

The proposal targets at MAPP Competition 1: Advancing Earth System Data Assimilation and
its Objective 2, by developing a new monitoring product on vegetation and carbon cycle. The
deliverable will be a monitoring product of vegetation dynamics and terrestrial carbon cycle at
0.5°×0.5° spatial resolution and hourly time step, provided as a best estimate analysis of blended
satellite and in-situ observations with predictive model. Underlying is a vegetation data
assimilation system that has the potential to provide real-time analysis to initialize vegetation
state for Earth system model prediction, thus contributing to NOAA’s long-term climate and
Earth system modeling goal.

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