128 research outputs found
Simultaneous assimilation of satellite and eddy covariance data for improving terrestrial water and carbon simulations at a semi-arid woodland site in Botswana
Terrestrial productivity in semi-arid woodlands is strongly susceptible to changes in precipitation, and semi-arid woodlands constitute an important element of the global water and carbon cycles. Here, we use the Carbon Cycle Data Assimilation System (CCDAS) to investigate the key parameters controlling ecological and hydrological activities for a semi-arid savanna woodland site in Maun, Botswana. Twenty-four eco-hydrological process parameters of a terrestrial ecosystem model are optimized against two data streams separately and simultaneously: daily averaged latent heat flux (LHF) derived from eddy covariance measurements, and decadal fraction of absorbed photosynthetically active radiation (FAPAR) derived from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS).
Assimilation of both data streams LHF and FAPAR for the years 2000 and 2001 leads to improved agreement between measured and simulated quantities not only for LHF and FAPAR, but also for photosynthetic CO2 uptake. The mean uncertainty reduction (relative to the prior) over all parameters is 14.9% for the simultaneous assimilation of LHF and FAPAR, 8.5% for assimilating LHF only, and 6.1% for assimilating FAPAR only. The set of parameters with the highest uncertainty reduction is similar between assimilating only FAPAR or only LHF. The highest uncertainty reduction for all three cases is found for a parameter quantifying maximum plant-available soil moisture. This indicates that not only LHF but also satellite-derived FAPAR data can be used to constrain and indirectly observe hydrological quantities.JRC.H.7-Climate Risk Managemen
Potts models in the continuum. Uniqueness and exponential decay in the restricted ensembles
In this paper we study a continuum version of the Potts model. Particles are
points in R^d, with a spin which may take S possible values, S being at least
3. Particles with different spins repel each other via a Kac pair potential. In
mean field, for any inverse temperature there is a value of the chemical
potential at which S+1 distinct phases coexist. For each mean field pure phase,
we introduce a restricted ensemble which is defined so that the empirical
particles densities are close to the mean field values. Then, in the spirit of
the Dobrushin Shlosman theory, we get uniqueness and exponential decay of
correlations when the range of the interaction is large enough. In a second
paper, we will use such a result to implement the Pirogov-Sinai scheme proving
coexistence of S+1 extremal DLR measures.Comment: 72 pages, 1 figur
Quantum walks on Cayley graphs
We address the problem of the construction of quantum walks on Cayley graphs.
Our main motivation is the relationship between quantum algorithms and quantum
walks. In particular, we discuss the choice of the dimension of the local
Hilbert space and consider various classes of graphs on which the structure of
quantum walks may differ. We completely characterise quantum walks on free
groups and present partial results on more general cases. Some examples are
given, including a family of quantum walks on the hypercube involving a
Clifford Algebra.Comment: J. Phys. A (accepted for publication
Simultaneous assimilation of satellite and eddy covariance data for improving terrestrial water and carbon simulations at a semi-arid woodland site in Botswana
Terrestrial productivity in semi-arid woodlands is strongly susceptible to changes in precipitation, and semi-arid woodlands constitute an important element of the global water and carbon cycles. Here, we use the Carbon Cycle Data Assimilation System (CCDAS) to investigate the key parameters controlling ecological and hydrological activities for a semi-arid savanna woodland site in Maun, Botswana. Twenty-four eco-hydrological process parameters of a terrestrial ecosystem model are optimized against two data streams separately and simultaneously: daily averaged latent heat flux (LHF) derived from eddy covariance measurements, and decadal fraction of absorbed photosynthetically active radiation (FAPAR) derived from the Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Assimilation of both data streams LHF and FAPAR for the years 2000 and 2001 leads to improved agreement between measured and simulated quantities not only for LHF and FAPAR, but also for photosynthetic CO2 uptake. The mean uncertainty reduction (relative to the prior) over all parameters is 14.9% for the simultaneous assimilation of LHF and FAPAR, 8.5% for assimilating LHF only, and 6.1% for assimilating FAPAR only. The set of parameters with the highest uncertainty reduction is similar between assimilating only FAPAR or only LHF. The highest uncertainty reduction for all three cases is found for a parameter quantifying maximum plant-available soil moisture. This indicates that not only LHF but also satellite-derived FAPAR data can be used to constrain and indirectly observe hydrological quantities
Land cover classification using multi-temporal MERIS vegetation indices
The spectral, spatial, and temporal resolutions of Envisat's Medium Resolution Imaging Spectrometer (MERIS) data are attractive for regional- to global-scale land cover mapping. Moreover, two novel and operational vegetation indices derived from MERIS data have considerable potential as discriminating variables in land cover classification. Here, the potential of these two vegetation indices (the MERIS global vegetation index (MGVI), MERIS terrestrial chlorophyll index (MTCI)) was evaluated for mapping eleven broad land cover classes in Wisconsin. Data acquired in the high and low chlorophyll seasons were used to increase inter-class separability. The two vegetation indices provided a higher degree of inter-class separability than data acquired in many of the individual MERIS spectral wavebands. The most accurate landcover map (73.2%) was derived from a classification of vegetation index-derived data with a support vector machine (SVM), and was more accurate than the corresponding map derived from a classification using the data acquired in the original spectral wavebands
Consistent retrieval of land surface radiation products from EO, including traceable uncertainty estimates
Earth
observation (EO) land surface products have been demonstrated to provide a
constraint on the terrestrial carbon cycle that is complementary to the
record of atmospheric carbon dioxide. We present the Joint Research Centre
Two-stream Inversion Package (JRC-TIP) for retrieval of variables
characterising the state of the vegetation–soil system. The system provides a
set of land surface variables that satisfy all requirements for assimilation
into the land component of climate and numerical weather prediction models.
Being based on a 1-D representation of the radiative transfer
within the canopy–soil system, such as those used in the land surface
components of advanced global models, the JRC-TIP products are not only
physically consistent internally, but they also achieve a high degree of
consistency with these global models. Furthermore, the products are provided
with full uncertainty information. We describe how these uncertainties are
derived in a fully traceable manner without any hidden assumptions from the
input observations, which are typically broadband white sky albedo products.
Our discussion of the product uncertainty ranges, including the uncertainty
reduction, highlights the central role of the leaf area index, which describes
the density of the canopy. We explain the generation of products aggregated
to coarser spatial resolution than that of the native albedo input and
describe various approaches to the validation of JRC-TIP products, including the
comparison against in situ observations. We present a JRC-TIP processing
system that satisfies all operational requirements and explain how it
delivers stable climate data records. Since many aspects of JRC-TIP are
generic,
the package can serve as an example of a state-of-the-art system for
retrieval of EO products, and this contribution can help the user to
understand advantages and limitations of such products
Organisational climates for diversity and their impacts on managerial attitudes and perceptions in the NHS and retail industry
Croplands cover about 12% of the ice-free terrestrial land surface.
Compared with natural ecosystems, croplands have distinct characteristics due
to anthropogenic influences. Their global gross primary production (GPP) is
not well constrained and estimates vary between 8.2 and
14.2 Pg C yr−1. We quantified global cropland GPP using a light use
efficiency (LUE) model, employing satellite observations and survey data of
crop types and distribution. A novel step in our analysis was to assign a
maximum light use efficiency estimate (ϵ*GPP) to
each of the 26 different crop types, instead of taking a uniform value as
done in the past. These ϵ*GPP values were
calculated based on flux tower CO2 exchange measurements and a
literature survey of field studies, and ranged from 1.20 to
2.96 g C MJ−1. Global cropland GPP was estimated to be
11.05 Pg C yr−1 in the year 2000. Maize contributed most to this
(1.55 Pg C yr−1), and the continent of Asia contributed most with
38.9% of global cropland GPP. In the continental United States, annual
cropland GPP (1.28 Pg C yr−1) was close to values reported previously
(1.24 Pg C yr−1) constrained by harvest records, but our estimates of
ϵ*GPP values were considerably higher. Our
results are sensitive to satellite information and survey data on crop type
and extent, but provide a consistent and data-driven approach to generate a
look-up table of ϵ*GPP for the 26 crop types for
potential use in other vegetation models
An Earth Observation Land Data Assimilation System (EO-LDAS)
Current methods for estimating vegetation parameters are generally sub-optimal in the way they exploit information and do not generally consider uncertainties. We look forward to a future where operational data assimilation schemes improve estimates by tracking land surface processes and exploiting multiple types of observations. Data assimilation schemes seek to combine observations and models in a statistically optimal way taking into account uncertainty in both, but have not yet been much exploited in this area.
The EO-LDAS scheme and prototype, developed under ESA funding, is designed to exploit the anticipated wealth of data that will be available under GMES missions, such as the Sentinel family of satellites, to provide improved mapping of land surface biophysical parameters. This paper describes the EO-LDAS implementation, and explores some of its core functionality. EO-LDAS is a weak constraint variational data assimilation system. The prototype provides a mechanism for constraint based on a prior estimate of the state vector, a linear dynamic model, and Earth Observation data (top-of-canopy reflectance here). The observation operator is a non-linear optical radiative transfer model for a vegetation canopy with a soil lower boundary, operating over the range 400 to 2500 nm. Adjoint codes for all model and operator components are provided in the prototype by automatic differentiation of the computer codes.
In this paper, EO-LDAS is applied to the problem of daily estimation of six of the parameters controlling the radiative transfer operator over the course of a year (> 2000 state vector elements). Zero and first order process model constraints are implemented and explored as the dynamic model. The assimilation estimates all state vector elements simultaneously. This is performed in the context of a typical Sentinel-2 MSI operating scenario, using synthetic MSI observations simulated with the observation operator, with uncertainties typical of those achieved by optical sensors supposed for the data.
The experiments consider a baseline state vector estimation case where dynamic constraints are applied, and assess the impact of dynamic constraints on the a posteriori uncertainties. The results demonstrate that reductions in uncertainty by a factor of up to two might be obtained by applying the sorts of dynamic constraints used here. The hyperparameter (dynamic model uncertainty) required to control the assimilation are estimated by a cross-validation exercise. The result of the assimilation is seen to be robust to missing observations with quite large data gaps
Climate controls on the variability of fires in the tropics and subtropics
In the tropics and subtropics, most fires are set by humans for a wide range of purposes. The total amount of burned area and fire emissions reflects a complex interaction between climate, human activities, and ecosystem processes. Here we used satellite-derived data sets of active fire detections, burned area, precipitation, and the fraction of absorbed photosynthetically active radiation (fAPAR) during 1998-2006 to investigate this interaction. The total number of active fire detections and burned area was highest in areas that had intermediate levels of both net primary production (NPP; 500-1000 g C
First-Order Phase Transition in Potts Models with finite-range interactions
We consider the -state Potts model on , , ,
with Kac ferromagnetic interactions and scaling parameter \ga. We prove the
existence of a first order phase transition for large but finite potential
ranges. More precisely we prove that for \ga small enough there is a value of
the temperature at which coexist Gibbs states. The proof is obtained by a
perturbation around mean-field using Pirogov-Sinai theory. The result is valid
in particular for , Q=3, in contrast with the case of nearest-neighbor
interactions for which available results indicate a second order phase
transition. Putting both results together provides an example of a system which
undergoes a transition from second to first order phase transition by changing
only the finite range of the interaction.Comment: Soumis pour publication a Journal of statistical physics - version
r\'{e}vis\'{e}
- …