Final Technical Report [Carbon Data Assimilation with a Coupled Ensemble Kalman Filter]

We proposed (and accomplished) the development of an Ensemble Kalman Filter (EnKF) approach for the estimation of surface carbon fluxes as if they were parameters, augmenting the model with them. Our system is quite different from previous approaches, such as carbon flux inversions, 4D-­‐Var, and EnKF with approximate background error covariance (Peters et al., 2008). We showed (using observing system simulation experiments, OSSEs) that these differences lead to a more accurate estimation of the evolving surface carbon fluxes at model grid-­‐scale resolution. The main properties of the LETKF-­‐C are: a) The carbon cycle LETKF is coupled with the simultaneous assimilation of the standard atmospheric variables, so that the ensemble wind transport of the CO2 provides an estimation of the carbon transport uncertainty. b) The use of an assimilation window (6hr) much shorter than the months-­‐long windows used in other methods. This avoids the inevitable “blurring” of the signal that takes place in long windows due to turbulent mixing since the CO2 does not have time to mix before the next window. In this development we introduced new, advanced techniques that have since been adopted by the EnKF community (Kang, 2009, Kang et al., 2011, Kang et al. 2012). These advances include “variable localization” that reduces sampling errors in the estimation of the forecast error covariance, more advanced adaptive multiplicative and additive inflations, and vertical localization based on the time scale of the processes. The main result has been obtained using the LETKF-­‐C with all these advances, and assimilating simulated atmospheric CO2 observations from different observing systems (surface flask observations of CO2 but no surface carbon fluxes observations, total column CO2 from GoSAT/OCO-­‐2, and upper troposphere AIRS retrievals). After a spin-­‐up of about one month, the LETKF-­‐C succeeded in reconstructing the true evolving surface fluxes of carbon at a model grid resolution. When applied to the CAM3.5 model, the LETKF gave very promising results as well, although only one month is available

The effects of the RAW filter on the climatology and forecast skill of the SPEEDY model

In a recent study, Williams introduced a simple modification to the widely used Robert–Asselin (RA) filter for numerical integration. The main purpose of the Robert–Asselin–Williams (RAW) filter is to avoid the undesired numerical damping of the RA filter and to increase the accuracy. In the present paper, the effects of the modification are comprehensively evaluated in the Simplified Parameterizations, Primitive Equation Dynamics (SPEEDY) atmospheric general circulation model. First, the authors search for significant changes in the monthly climatology due to the introduction of the new filter. After testing both at the local level and at the field level, no significant changes are found, which is advantageous in the sense that the new scheme does not require a retuning of the parameterized model physics. Second, the authors examine whether the new filter improves the skill of short- and medium-term forecasts. January 1982 data from the NCEP–NCAR reanalysis are used to evaluate the forecast skill. Improvements are found in all the model variables (except the relative humidity, which is hardly changed). The improvements increase with lead time and are especially evident in medium-range forecasts (96–144 h). For example, in tropical surface pressure predictions, 5-day forecasts made using the RAW filter have approximately the same skill as 4-day forecasts made using the RA filter. The results of this work are encouraging for the implementation of the RAW filter in other models currently using the RA filter

Ensemble transform Kalman-Bucy filters

Two recent works have adapted the Kalman-Bucy filter into an ensemble setting. In the first formulation, BR10, the full ensemble is updated in the analysis step as the solution of single set of ODEs in pseudo-BGR09, the ensemble of perturbations is updated by the solution of an ordinary differential equation (ODE) in pseudo-time, while the mean is updated as in the standard KF. In the second formulation, BR10, the full ensemble is updated in the analysis step as the solution of single set of ODEs in pseudo-time. Neither requires matrix inversions except for the frequently diagonal observation error covariance. We analyze the behavior of the ODEs involved in these formulations. We demonstrate that they stiffen for large magnitudes of the ratio of background to observational error covariance, and that using the integration scheme proposed in both BGR09 and BR10 can lead to failure. An integration scheme that is both stable and is not computationally expensive is proposed. We develop transform-based alternatives for these Bucy-type approaches so that the integrations are computed in ensemble space where the variables are weights (of dimension equal to the ensemble size) rather than model variables. Finally, the performance of our ensemble transform Kalman-Bucy implementations is evaluated using three models: the 3-variable Lorenz 1963 model, the 40-variable Lorenz 1996 model, and a medium complexity atmospheric general circulation model (AGCM) known as SPEEDY. The results from all three models are encouraging and warrant further exploration of these assimilation techniques

Brief Communication: Breeding vectors in the phase space reconstructed from time series data

Bred vectors characterize the nonlinear instability of dynamical systems and so far have been computed only for systems with known evolution equations. In this article, bred vectors are computed from a single time series data using time-delay embedding, with a new technique, nearest-neighbor breeding. Since the dynamical properties of the standard and nearest-neighbor breeding are shown to be similar, this provides a new and novel way to model and predict sudden transitions in systems represented by time series data alone

Estimating surface carbon fluxes based on a local ensemble transform Kalman filter with a short assimilation window and a long observation window: an observing system simulation experiment test in GEOS-Chem 10.1

We developed a carbon data assimilation system to estimate surface carbon fluxes using the local ensemble transform Kalman filter (LETKF) and atmospheric transport model GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological field based on the Goddard Earth Observing System model, version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an observing system simulation experiment (OSSE) as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation window of 6 h. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as “variable localization”, and increased observation weights near the surface, they obtained accurate surface carbon fluxes at grid-point resolution. We developed a new version of the local ensemble transform Kalman filter related to the “running-in-place” (RIP) method used to accelerate the spin-up of ensemble Kalman filter (EnKF) data assimilation (Kalnay and Yang, 2010; Wang et al., 2013; Yang et al., 2012). Like RIP, the new assimilation system uses the “no cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting the Kalman filter solution forward or backward within an assimilation window at no cost. In the new scheme a long “observation window” (e.g., 7 d or longer) is used to create a LETKF ensemble at 7 d. Then, the RIP smoother is used to obtain an accurate final analysis at 1 d. This new approach has the advantage of being based on a short assimilation window, which makes it more accurate, and of having been exposed to the future 7 d observations, which improves the analysis and accelerates the spin-up. The assimilation and observation windows are then shifted forward by 1 d, and the process is repeated. This reduces significantly the analysis error, suggesting that the newly developed assimilation method can be used with other Earth system models, especially in order to make greater use of observations in conjunction with models

Local cooling and warming effects of forests based on satellite observations

The biophysical effects of forests on climate have been extensively studied with climate models. However, models cannot accurately reproduce local climate effects due to their coarse spatial resolution and uncertainties, and field observations are valuable but often insufficient due to their limited coverage. Here we present new evidence acquired from global satellite data to analyse the biophysical effects of forests on local climate. Results show that tropical forests have a strong cooling effect throughout the year; temperate forests show moderate cooling in summer and moderate warming in winter with net cooling annually; and boreal forests have strong warming in winter and moderate cooling in summer with net warming annually. The spatiotemporal cooling or warming effects are mainly driven by the two competing biophysical effects, evapotranspiration and albedo, which in turn are strongly influenced by rainfall and snow. Implications of our satellite-based study could be useful for informing local forestry policies

Sensitivity of different configurations of an ensemble based data assimilation system implemented over southern South America

Uno de los mayores desafíos en la predicción numérica del tiempo es el de reducir la incertidumbre de las condiciones iniciales. Con el fin de abordar esta problemática, variados esfuerzos se están llevando a cabo en el Servicio Meteorológico Nacional de Argentina (SMN). En este artículo se presenta la evaluación del sistema regional de asimilación por ensambles WRF-LETKF (Weather Research and Forecasting model - Local Ensemble Transform Kalman Filter). El dominio cubre el Sur de Sudamérica con una resolución horizontal de 40 km, y el período de prueba utilizado es de dos meses (noviembre y diciembre de 2012). El sistema de asimilación consta de un ensamble de 40 miembros e incorpora observaciones tanto convencionales como provenientes de satélites.En este trabajo, se evaluó el impacto de utilizar un ensamble multi física incluyendo en sus miembros distintas opciones de parametrizaciones de cumulus y capa límite planetaria. Se halló que dicha estrategia generalmente produce resultados mejores comparada con un sistema de ensamble en el cual todos los miembros poseen las mismas parametrizaciones. También se exploró la inclusión de bordes perturbados, pero no se encontró un impacto significativo con la metodología propuesta. Otro experimento consistió en la inclusión de los perfiles verticales de temperatura y humedad de los AIRS(Atmospheric Infrared Sounders) en la asimilación, cuya evaluación demostró un impacto positivo en los resultados. Finalmente, se comparó la media de los pronósticos por ensamble inicializados con los análisis de las diferentes variantes del sistema WRF-LETKF con un pronóstico determinístico del WRF inicializado con los análisis provistos por el GFS (Global Forecast System). Si bien generalmente dicha comparación mostró un impacto positivo de la asimilación de datos a escala regional, también mostró la necesidad de que el sistema regional mantenga la información de mayor escala provista por el modelo global.One of the big challenges in numerical weather prediction is to reduce the uncertainty in the initial conditions. At the National Meteorological Service (SMN) of Argentina, many efforts have been carried out to address this issue. In this work, the regional Local Ensemble Transform Kalman Filter coupled with the Weather Research and Forecasting model (WRF-LETKF) system is evaluated. The domain covers most of Southern South America with an horizontal resolution of 40 km and a 2 month period is tested (November and December 2012). A 40 member ensemble is used to assimilate conventional and satellite observations. In this work a multi physics ensemble that includes different choices for the cumulus and planetary boundary layer parameterizations is evaluated. This experiment shows that, overall, the multi physics approach produce better results than a single physics configuration. The inclusion of boundary perturbations has also been explored although, it does not produce a significant impact in the current experimental settings. In addition, we explore the sensitivity to the assimilation of the Atmospheric Infrared Sounder (AIRS) temperature and moisture retrievals. The results indicate that the inclusion of these retrievals is a valuable alternative to deal with the scarcity of radiosondes observations in Southern South America. Finally, a comparison among the different WRF-LETKF ensemble mean forecasts and deterministic WRF forecasts initialized from the GFS (Global Forecast System) without assimilation, was carried on. Generally a positive impact of the data assimilation technique was achieved, although it was found that the regional system needs to keep large scale information from the global model.Fil: Dillon, María Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Ministerio de Defensa. Secretaria de Planeamiento. Servicio Meteorológico Nacional. Servicio Metereológico Nacional (sede Dorrego).; ArgentinaFil: Garcia Skabar, Yanina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Ministerio de Defensa. Secretaria de Planeamiento. Servicio Meteorológico Nacional. Servicio Metereológico Nacional (sede Dorrego).; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; ArgentinaFil: Kalnay, Eugenia. University of Maryland; Estados UnidosFil: Ruiz, Juan Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; ArgentinaFil: Collini, Estela Angela. Ministerio de Defensa. Armada Argentina. Servicio de Hidrografía Naval; Argentin

Comparison between Local Ensemble Transform Kalman Filter and PSAS in the NASA finite volume GCM: perfect model experiments

This paper explores the potential of Local Ensemble Transform Kalman Filter (LETKF) by comparing the performance of LETKF with an operational 3D-Var assimilation system, Physical-Space Statistical Analysis System (PSAS), under a perfect model scenario. The comparison is carried out on the finite volume Global Circulation Model (fvGCM) with 72 grid points zonally, 46 grid points meridionally and 55 vertical levels. With only forty ensemble members, LETKF obtains an analysis and forecasts with lower RMS errors than those from PSAS. The performance of LETKF is further improved, especially over the oceans, by assimilating simulated temperature observations from rawinsondes and conventional surface pressure observations instead of geopotential heights. An initial decrease of the forecast errors in the NH observed in PSAS but not in LETKF suggests that the PSAS analysis is less balanced. The observed advantage of LETKF over PSAS is due to the ability of the forty-member ensemble from LETKF to capture flow-dependent errors and thus create a good estimate of the true background uncertainty. Furthermore, localization makes LETKF highly parallel and efficient, requiring only 5 minutes per analysis in a cluster of 20 PCs with forty ensemble members.Comment: 50 pages, 11 figure