Tim Hoar - Profile on Academia.edu (original) (raw)

Papers by Tim Hoar

Regional and basin scale applications of ensemble adjustment Kalman filter and 4D-Var ocean data assimilation systems

Progress in Oceanography

Estimating continental scaling of ecological driver-response feedbacks through model-data fusion

POPDART: An Ensemble Data Assimilation System for the Ocean Component of CESM

ABSTRACT

The impacts of uncertainty in observations on a data assimilation system for ecological forecasting

Background/Question/Methods The National Ecological Observatory Network (NEON) is a continental-s... more Background/Question/Methods The National Ecological Observatory Network (NEON) is a continental-scale facility that will collect ecological data, including eddy covariance flux observations, from 60 sites in the continental US, Alaska, Hawaii, and Puerto Rico over 30 years. We are vigorously quantifying uncertainty in all NEON data products and are investigating the propagation of uncertainty from basic observations when generating high-level data products, such as continental-scale, gridded maps of carbon and energy fluxes. One approach we are using to integrate many observational data streams into high-level data products is to use model-data fusion. In this approach a process model is used to provide an analytical framework for data interpretation, synthesis, interpolation and extrapolation. In theory, uncertainty from many sources can be accounted for including: (i) observational data, due to incomplete and noisy observations and biases; (ii) process model structure; (iii) proce...

Quantifying uncertainty in projections of continental fluxes of carbon and energy using the NEON platform

Background/Question/Methods As we enter a new “data rich” period for ecological science new possi... more Background/Question/Methods As we enter a new “data rich” period for ecological science new possibilities become available which allow for better forecasting of many ecosystem processes. We are utilizing data from the continental-scale NEON platform and other monitoring networks (FLUXNET, ICOS, LTER etc.), in conjunction with ever-increasing computing power, land surface model sophistication and new statistical and optimization methodologies to address a pressing societal need for improved quantification and reduction of uncertainty of projections of carbon and energy fluxes across the US. We have developed a data assimilation system coupling the Community Land Model (CLM) with the Data Assimilation Research Testbed (DART), an advanced facility for ensemble data assimilation (DA). Using this new tool we are able to constrain the model with data to give predictions that best approximate the observations. Using this approach, in theory we able to account for multiple sources of uncert...

Passive microwave radiance data assimilation for estimating snow water equivalent over North America

Snow is a critical component of the global energy and water balances, in particular at middle to ... more Snow is a critical component of the global energy and water balances, in particular at middle to high latitudes, because of snow’s high albedo, low thermal conductivity, and water holding capacity. Data assimilation has been an important tool to obtain the distribution of snow depth and snow water equivalent (SWE), which are critical for climate and water resource applications. In previous studies, passive microwave (PM) radiance assimilation (RA) has shown promise for improving SWE estimations at point, local, and basin scales. In this study, we aim to address the feasibility of RA to improve SWE estimates at the continental scale. We use the Community Land Model version 4 (CLM4) for snow dynamics and the Dense Media Radiative Transfer–Multi Layers model (DMRT-ML) for snowpack brightness temperature (TB) estimations. Atmospheric and vegetation radiative transfer models (RTMs) are also incorporated to consider the effects of atmosphere and vegetation on TB at the top of the atmosphe...

Aboveground cosmic-ray neutron measurements provide an opportunity to infer soil moisture at the ... more Aboveground cosmic-ray neutron measurements provide an opportunity to infer soil moisture at the sub-kilometer scale. Initial efforts to assimilate those measurements have shown promise. This study expands such analysis by investigating (1) how the information from aboveground cosmic-ray neutrons can constrain the soil moisture at 5 15 servations at the semi-arid site. However, soil moisture profiles are better constrained when assimilating synthetic cosmic-ray neutrons observations hourly rather than every 2 days at the cropland and mixed forest sites. This indicates potential benefits for hydrometeorological modeling when soil moisture measurements are available at relatively high frequency. Moreover, differences in summertime meteorological forcing 20 between the semi-arid site and the other two sites may indicate a possible controlling factor to soil moisture dynamics in addition to differences in soil and vegetation properties.

DART: A Community Facility for Ensemble Data Assimilation

The Data Assimilation Research Testbed (DART) is a mature community software facility providing r... more The Data Assimilation Research Testbed (DART) is a mature community software facility providing researchers access to state-of-the-art ensemble data assimilation tools. The freely-available DART distribution includes fully functional low-order and high-order models, support for commonly available observations, hooks to easily add both new models and observation types, diagnostic programs to interpret the results, and a full tutorial suitable for self-study

Tellus A, 1998

A fast, eastward signal in the surface zonal winds of the equatorial Pacific ocean is identified ... more A fast, eastward signal in the surface zonal winds of the equatorial Pacific ocean is identified in ERS-1 data that coincide with an intraseasonal tropical oscillation during the intensive observing period of TOGA COARE. The fast eastward event is compared with observations for the same time period from the TAO moorings at the equator. Similar events are identified in longer records of the ERS-1 and TAO data. A composite event is constructed and a lagcorrelation analysis is used to infer basin-scale propagation speed. The fast eastward events in the ERS-1 data are consistent with signals in station surface pressure and station profile data quantified by Milliff and Madden and connected with first-baroclinic mode equatorial Kelvin wave propagation.

Journal of Geophysical Research: Atmospheres, 2014

To improve snowpack estimates in Community Land Model version 4 (CLM4), the Moderate Resolution I... more To improve snowpack estimates in Community Land Model version 4 (CLM4), the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) was assimilated into the Community Land Model version 4 (CLM4) via the Data Assimilation Research Testbed (DART). The interface between CLM4 and DART is a flexible, extensible approach to land surface data assimilation. This data assimilation system has a large ensemble (80-member) atmospheric forcing that facilitates ensemble-based land data assimilation. We use 40 randomly chosen forcing members to drive 40 CLM members as a compromise between computational cost and the data assimilation performance. The localization distance, a parameter in DART, was tuned to optimize the data assimilation performance at the global scale. Snow water equivalent (SWE) and snow depth are adjusted via the ensemble adjustment Kalman filter, particularly in regions with large SCF variability. The root-mean-square error of the forecast SCF against MODIS SCF is largely reduced. In DJF (December-January-February), the discrepancy between MODIS and CLM4 is broadly ameliorated in the lower-middle latitudes (23°-45°N). Only minimal modifications are made in the higher-middle (45°-66°N) and high latitudes, part of which is due to the agreement between model and observation when snow cover is nearly 100%. In some regions it also reveals that CLM4-modeled snow cover lacks heterogeneous features compared to MODIS. In MAM (March-April-May), adjustments to snow move poleward mainly due to the northward movement of the snowline (i.e., where largest SCF uncertainty is and SCF assimilation has the greatest impact). The effectiveness of data assimilation also varies with vegetation types, with mixed performance over forest regions and consistently good performance over grass, which can partly be explained by the linearity of the relationship between SCF and SWE in the model ensembles. The updated snow depth was compared to the Canadian Meteorological Center (CMC) data. Differences between CMC and CLM4 are generally reduced in densely monitored regions.

Journal of Climate, 2013

The authors report on the implementation and evaluation of a 48-member ensemble adjustment Kalman... more The authors report on the implementation and evaluation of a 48-member ensemble adjustment Kalman filter (EAKF) for the ocean component of the Community Climate System Model, version 4 (CCSM4). The ocean assimilation system described was developed to support the eventual generation of historical oceanstate estimates and ocean-initialized climate predictions with the CCSM4 and its next generation, the Community Earth System Model (CESM). In this initial configuration of the system, daily subsurface temperature and salinity data from the 2009 World Ocean Database are assimilated into the ocean model from 1 January 1998 to 31 December 2005. Each ensemble member of the ocean is forced by a member of an independently generated CCSM4 atmospheric EAKF analysis, making this a loosely coupled framework. Over most of the globe, the time-mean temperature and salinity fields are improved relative to an identically forced ocean model simulation without assimilation. This improvement is especially notable in strong frontal regions such as the western and eastern boundary currents. The assimilation system is most effective in the upper 1000 m of the ocean, where the vast majority of in situ observations are located. Because of the shortness of this experiment, ocean variability is not discussed. Challenges that arise from using an ocean model with strong regional biases, coarse resolution, and low internal variability to assimilate real observations are discussed, and areas of ongoing improvement for the assimilation system are outlined.

Dynamics of Atmospheres and Oceans, 2013

This paper describes the development of an advanced ensemble Kalman filter (EnKF)-based ocean dat... more This paper describes the development of an advanced ensemble Kalman filter (EnKF)-based ocean data assimilation system for prediction of the evolution of the loop current in the Gulf of Mexico (GoM). The system integrates the Data Assimilation Research Testbed (DART) assimilation package with the Massachusetts Institute of Technology ocean general circulation model (MITgcm). The MITgcm/DART system supports the assimilation of a wide range of ocean observations and uses an ensemble approach to solve the nonlinear assimilation problems. The GoM prediction system was implemented with an eddy-resolving 1/10th degree configuration of the MITgcm. Assimilation experiments were performed over a 6-month period between May and October during a strong loop current event in 1999. The model was sequentially constrained with weekly satellite sea surface temperature and altimetry data. Experiments results suggest that the ensemble-based assimilation system shows a high predictive skill in the GoM, with estimated ensemble spread mainly concentrated around the front of the loop current. Further analysis of the system estimates demonstrates that the ensemble assimilation accurately reproduces the observed features without imposing any negative impact on the dynamical balance of the system. Results from sensitivity experiments with respect to the ensemble filter parameters are also presented and discussed.

AGU Fall Meeting Abstracts, Dec 1, 2008

The ECCO system is a new generation of ocean assimilation systems based on the Massachusetts Inst... more The ECCO system is a new generation of ocean assimilation systems based on the Massachusetts Institute of Technology general circulation model (MITgcm) and its adjoint. The system has been used to produce the first global 1o ocean state estimates (Köhl et al., 2006 and Wunsch and Heimbach, 2008). It is now also used for regional and coastal MITgcm applications (Hoteit et al., 2005; Gebbie et al., 2006; Hoteit et al., 2008). To improve the predictive capabilities of the ECCO system, the Data Assimilation Research Testbed ( ...

Bulletin of the American Meteorological Society, 2009