Evaluation of the Weak Constraint Data Assimilation Approach for Estimating Turbulent Heat Fluxes at Six Sites

A number of studies have estimated turbulent heat fluxes by assimilating sequences of land surface temperature (LST) observations into the strong constraint-variational data assimilation (SC-VDA) approaches. The SC-VDA approaches do not account for the structural model errors and uncertainties in the micrometeorological variables. In contrast to the SC-VDA approaches, the WC-VDA approach (the so-called weak constraint-VDA) accounts for the effects of structural and model errors by adding a model error term. In this study, the WC-VDA approach is tested at six study sites with different climatic and vegetative conditions. Its performance is also compared with that of SC-VDA at the six study sites. The results show that the WC-VDA produces 10.16% and 10.15% lower root mean square errors (RMSEs) for sensible and latent heat flux estimates compared with the SC-VDA approach. The model error term can capture errors in the turbulent heat flux estimates due to errors in LST and micrometeorological measurements, as well as structural model errors, and does not allow those errors to adversely affect the turbulent heat flux estimates. The findings also indicate that the estimated model error term varies reasonably well, so as to capture the misfit between predicted and observed net radiation in different hydrological and vegetative conditions. Finally, synthetically generated positive (negative) noises are added to the hydrological input variables (e.g., LST, air temperature, air humidity, incoming solar radiation, and wind speed) to examine whether the WC-VDA approach can capture those errors. It was found that the WC-VDA approach accounts for the errors in the input data and reduces their effect on the turbulent heat flux estimates

Mapping Regional Turbulent Heat Fluxes via Assimilation of MODIS Land Surface Temperature Data into an Ensemble Kalman Smoother Framework

Estimation of turbulent heat fluxes via variational data assimilation (VDA) approaches has been the subject of several studies. The VDA approaches need an adjoint model that is difficult to derive. In this study, remotely sensed land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are assimilated into the heat diffusion equation within an ensemble Kalman smoother (EnKS) approach to estimate turbulent heat fluxes. The EnKS approach is tested in the Heihe River Basin (HRB) in northwest China. The results show that the EnKS approach can estimate turbulent heat fluxes by assimilating low temporal resolution LST data from MODIS. The findings indicate that the EnKS approach performs fairly well in various hydrological and vegetative conditions. The estimated sensible (H) and latent (LE) heat fluxes are compared with the corresponding observations from large aperture scintillometer systems at three sites (namely, Arou, Daman, and Sidaoqiao) in the HRB. The turbulent heat flux estimates from EnKS agree reasonably well with the observations, and are comparable to those of the VDA approach. The EnKS approach also provides statistical information on the H and LE estimates. It is found that the uncertainties of H and LE estimates are higher over wet and/or densely vegetated areas (grassland and forest) compared to the dry and/or slightly vegetated areas (cropland, shrubland, and barren land)

ESTIMATION OF TURBULENT HEAT FLUXES VIA THE SYNERGISTIC ASSIMILATION OF LAND SURFACE TEMPERATURE, AIR TEMPERATURE AND SPECIFIC HUMIDITY INTO A VARIATIONAL DATA ASSIMILATION MODEL

Ph.D

Estimation of surface turbulent heat fluxes via variational assimilation of sequences of land surface temperatures from Geostationary Operational Environmental Satellites

Recently, a number of studies have focused on estimating surface turbulent heat fluxes via assimilation of sequences of land surface temperature (LST) observations into variational data assimilation (VDA) schemes. Using the full heat diffusion equation as a constraint, the surface energy balance equation can be solved via assimilation of sequences of LST within a VDA framework. However, the VDA methods have been tested only in limited field sites that span only a few climate and land use types. Hence, in this study, combined-source (CS) and dual-source (DS) VDA schemes are tested extensively over six FluxNet sites with different vegetation covers (grassland, cropland, and forest) and climate conditions. The CS model groups the soil and canopy together as a single source and does not consider their different contributions to the total turbulent heat fluxes, while the DS model considers them to be different sources. LST data retrieved from the Geostationary Operational Environmental Satellites are assimilated into these two VDA schemes. Sensible and latent heat flux estimates from the CS and DS models are compared with the corresponding measurements from flux tower stations. The results indicate that the performance of both models at dry, lightly vegetated sites is better than that at wet, densely vegetated sites. Additionally, the DS model outperforms the CS model at all sites, implying that the DS scheme is more reliable and can characterize the underlying physics of the problem better

Estimation of turbulent surface heat fluxes using sequences of remotely sensed land surface temperature

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 330-366).Fluxes of heat and moisture at the land-surface play a significant role in the climate system. These fluxes interact with the overlying atmosphere and influence the characteristics of the planetary boundary layer (e.g. temperature and water vapor content), ultimately influencing the presence and growth of low level clouds and precipitation. Nevertheless, there is currently no network of in situ sensors that can map these fluxes globally. Recently a number of studies have focused on the estimation of surface energy flux components based on the assimilation of land surface temperature (LST) within a variational data assimilation (VDA) framework. This study provides the theoretical basis for why sequences of LST contain the necessary information to estimate surface fluxes with minimal reliance on ancillary data and empirical parameterizations. Furthermore this study addresses one of the main drawbacks of the existing VDA models. They use the simple force-restore equation for soil heat diffusion as a physical constraint. The force-restore equation provides a simplified description of the LST dynamics. Also, its performance is highly affected by the specification of a deep ground temperature. These shortcomings cause significant errors in the diurnal dynamics of heat diffusion in the soil and ultimately the retrieval of components of surface energy balance. This study advances the VDA scheme by using the full heat diffusion equation as a constraint in lieu of the forcerestore approximation. The new VDA scheme is tested over several experimental field sites. The results show that inclusion of the heat diffusion equation decreases the phase error associated with the ground heat flux diurnal cycle, and improves surface heat flux estimation. The VDA scheme is further advanced by incorporating model uncertainty in order to account for measurement and model errors. Tests indicate that the VDA scheme with model uncertainty captures measurement errors as well as structural model errors. In order to provide coupled estimates of surface heat fluxes and vegetation dynamics, remotely sensed LST and fraction of photosynthetically active radiation are assimilated into a surface energy balance and a vegetation dynamics model. The application of the assimilation over West Africa shows that the scheme provides reliable estimates of important vegetation dynamics parameters that are required for understanding the role of plant phenology on surface energy balance and vice-versa.by Sayed Mohyeddin Bateni.Ph.D

An assessment of air-sea heat fluxes from ocean and coupled reanalyses

Sixteen monthly air–sea heat flux products from global ocean/coupled reanalyses are compared over 1993–2009 as part of the Ocean Reanalysis Intercomparison Project (ORA-IP). Objectives include assessing the global heat closure, the consistency of temporal variability, comparison with other flux products, and documenting errors against in situ flux measurements at a number of OceanSITES moorings. The ensemble of 16 ORA-IP flux estimates has a global positive bias over 1993–2009 of 4.2 ± 1.1 W m−2. Residual heat gain (i.e., surface flux + assimilation increments) is reduced to a small positive imbalance (typically, +1–2 W m−2). This compensation between surface fluxes and assimilation increments is concentrated in the upper 100 m. Implied steady meridional heat transports also improve by including assimilation sources, except near the equator. The ensemble spread in surface heat fluxes is dominated by turbulent fluxes (>40 W m−2 over the western boundary currents). The mean seasonal cycle is highly consistent, with variability between products mostly <10 W m−2. The interannual variability has consistent signal-to-noise ratio (~2) throughout the equatorial Pacific, reflecting ENSO variability. Comparisons at tropical buoy sites (10°S–15°N) over 2007–2009 showed too little ocean heat gain (i.e., flux into the ocean) in ORA-IP (up to 1/3 smaller than buoy measurements) primarily due to latent heat flux errors in ORA-IP. Comparisons with the Stratus buoy (20°S, 85°W) over a longer period, 2001–2009, also show the ORA-IP ensemble has 16 W m−2 smaller net heat gain, nearly all of which is due to too much latent cooling caused by differences in surface winds imposed in ORA-IP

Assessing Evapotranspiration Estimates from the Global Soil Wetness Project Phase 2 (GSWP-2) Simulations

Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).We assess the simulations of global-scale evapotranspiration from the Global Soil Wetness Project Phase 2 (GSWP-2) within a global water-budget framework. The scatter in the GSWP-2 global evapotranspiration estimates from various land surface models can constrain the global, annual water budget fluxes to within ±2.5%, and by using estimates of global precipitation, the residual ocean evaporation estimate falls within the range of other independently derived bulk estimates. However, the GSWP-2 scatter cannot entirely explain the imbalance of the annual fluxes from a modern-era, observationally-based global water budget assessment, and inconsistencies in the magnitude and timing of seasonal variations between the global water budget terms are found. Inter-model inconsistencies in evapotranspiration are largest for high latitude inter-annual variability as well as for inter-seasonal variations in the tropics, and analyses with field-scale data also highlights model disparity at estimating evapotranspiration in high latitude regions. Analyses of the sensitivity simulations that replace uncertain forcings (i.e. radiation, precipitation, and meteorological variables) indicate that global (land) evapotranspiration is slightly more sensitive to precipitation than net radiation perturbations, and the majority of the GSWP-2 models, at a global scale, fall in a marginally moisture-limited evaporative condition. Finally, the range of global evapotranspiration estimates among the models is larger than any bias caused by uncertainties in the GSWP-2 atmospheric forcing, indicating that model structure plays a more important role toward improving global land evaporation estimates (as opposed to improved atmospheric forcing).NASA Energy and Water-cycle Study (NEWS, grant #NNX06AC30A), under the NEWS Science and Integration Team activities

Impact of climate and anthropogenic effects on the energy, water, and carbon budgets of monitored agrosystems: multi-site analysis combining modelling and experimentation

Les terres cultivées représentent une unité importante dans le climat mondial, et en réponse à la population, elles sont en expansion. Il est crucial de comprendre et de quantifier les interactions terre-atmosphère via les échanges d'eau, d'énergie et de carbone. Dans ce contexte, cette thèse a consisté à étudier la variabilité du bilan énergétique en fonction de différentes cultures, phénologies et pratiques agricoles via système Eddy-Covariance. En réponse au manque d'eau dans le sud-ouest de la France, deux modèles de surface (ISBA et ISBA-MEB) ont été évalués sur deux cultures (blé et maïs) pour évaluer leur capacité à estimer les flux d'énergie et d'eau. Enfin, en réponse à la contribution des terres cultivées à l'augmentation du dioxyde de carbone atmosphérique, la capacité du modèle ISBA-MEB à simuler correctement les principaux composants du carbone a été testée sur 11 saisons de maïs et de blé.Croplands represent an important unit within the global climate, and in response to population, they are expanding. Hence, understanding and quantifying the land-atmosphere interactions via water, energy and carbon exchanges is crucial. In this context, the first objective of this thesis studied the variability of the energy balance over different crops, phenologies, and farm practices at Lamasquère and Auradé. Secondly, in response to water scarcity and increasing drought in southwestern France, two land surface models (ISBA and ISBA-MEB) of different configurations were evaluated over some wheat and maize years to test their ability to estimate energy and water fluxes using measurements from an eddy covariance system as reference. Finally, in response to the contribution of croplands to increasing atmospheric carbon dioxide, the capability of the ISBA-MEB model to correctly simulate the major carbon components was tested over 11 seasons of maize and wheat

Development of a Benchmark Eddy Flux Evapotranspiration Dataset for Evaluation of Satellite-Driven Evapotranspiration Models Over the CONUS

A large sample of ground-based evapotranspiration (ET) measurements made in the United States, primarily from eddy covariance systems, were post-processed to produce a benchmark ET dataset. The dataset was produced primarily to support the intercomparison and evaluation of the OpenET satellite-based remote sensing ET (RSET) models and could also be used to evaluate ET data from other models and approaches. OpenET is a web-based service that makes field-delineated and pixel-level ET estimates from well-established RSET models readily available to water managers, agricultural producers, and the public. The benchmark dataset is composed of flux and meteorological data from a variety of providers covering native vegetation and agricultural settings. Flux footprint predictions were developed for each station and included static flux footprints developed based on average wind direction and speed, as well as dynamic hourly footprints that were generated with a physically based model of upwind source area. The two footprint prediction methods were rigorously compared to evaluate their relative spatial coverage. Data from all sources were post-processed in a consistent and reproducible manner including data handling, gap-filling, temporal aggregation, and energy balance closure correction. The resulting dataset included 243,048 daily and 5,284 monthly ET values from 194 stations, with all data falling between 1995 and 2021. We assessed average daily energy imbalance using 172 flux sites with a total of 193,021 days of data, finding that overall turbulent fluxes were understated by about 12% on average relative to available energy. Multiple linear regression analyses indicated that daily average latent energy flux may be typically understated slightly more than sensible heat flux. This dataset was developed to provide a consistent reference to support evaluation of RSET data being developed for a wide range of applications related to water accounting and water resources management at field to watershed scales