Showing papers by "Eugenia Kalnay published in 2019"

The Ensemble Mars Atmosphere Reanalysis System (EMARS) Version 1.0.

Abstract: The Ensemble Mars Atmosphere Reanalysis System (EMARS) dataset version 1.0 contains hourly gridded atmospheric variables for the planet Mars, spanning Mars Year (MY) 24 through 33 (1999 through 2017). A reanalysis represents the best estimate of the state of the atmosphere by combining observations that are sparse in space and time with a dynamical model and weighting them by their uncertainties. EMARS uses the Local Ensemble Transform Kalman Filter (LETKF) for data assimilation with the GFDL/NASA Mars Global Climate Model (MGCM). Observations that are assimilated include the Thermal Emission Spectrometer (TES) and Mars Climate Sounder (MCS) temperature retrievals. The dataset includes gridded fields of temperature, wind, surface pressure, as well as dust, water ice, CO2 surface ice and other atmospheric quantities. Reanalyses are useful for both science and engineering studies, including investigations of transient eddies, the polar vortex, thermal tides and dust storms, and during spacecraft operations.

Temperature and Salinity Variability in the SODA3, ECCO4r3, and ORAS5 Ocean Reanalyses, 1993–2015

Abstract: This study extends recent ocean reanalysis comparisons to explore improvements to several next-generation products, the Simple Ocean Data Assimilation, version 3 (SODA3); the Estimating the...

Neural machine-based forecasting of chaotic dynamics

Abstract: Chaotic dynamics is ubiquitous in nature. Traditionally, a good model representation of a certain system can help in predicting this system’s future behavior. However, for a complex system, a physics-based model may not be easy to construct given the complexity of a system, in particular, those that exhibit chaotic behavior. Furthermore, due to the aperiodic nature of the motion and finite precision, a model-based prediction may only have relatively high accuracy over a short-time horizon, before significant growth of error occurs in the prediction. In this article, the authors explore an alternate modeling approach, which is based on data-driven modeling, to explore forecasting viability for systems that display chaotic dynamics. Specifically, a deep recurrent neural network architecture, a neural machine, is constructed for forecasting temporal evolution of different chaotic systems. Data obtained from simulations with well-known nonlinear dynamical system prototypes serve as training data for the chosen neural network. In practice, this simulation data may be replaced with field data. The trained system is studied to examine the forecasting ability. Two ordinary differential dynamical systems, namely the Lorenz’63 system and the Lorenz’96 system, and a partial differential system, the Kuramoto–Sivashinsky equation, are studied, and the numerical experiments conducted are presented here to demonstrate the forecasting viability of the constructed neural network.

Local Atmosphere–Ocean Predictability: Dynamical Origins, Lead Times, and Seasonality

Abstract: Due to the physical coupling between atmosphere and ocean, information about the ocean helps to better predict the future of the atmosphere, and in turn, information about the atmosphere he...

Proactive Quality Control: Observing System Simulation Experiments with the Lorenz ’96 Model

Abstract: Proactive quality control (PQC) is a fully flow-dependent QC for observations based on the ensemble forecast sensitivity to observations technique (EFSO). It aims at reducing the forecast s...

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

Abstract: . 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.

Sensibilidad de un sistema de asimilación de datos por ensambles a diferentes configuraciones, implementado en el Sur de Sudamérica

Abstract: 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.

SODA Project: SODA3 Ensemble Means and Standard Deviations

Abstract: The goal of SODA is to reconstruct the historical physical (and eventually biogeochemical) history of the ocean since the beginning of the 20th century. As its name implies, the Simple Ocean Data Assimilation ocean/sea ice reanalysis (SODA) uses a simple architecture based on community standard codes with resolution chosen to match available data and the scales of motion that are resolvable. Agreement with direct measurements (to within observational error estimates) as well as unbiased statistics are expected. While SODA remains a university-based research project, an objective is to support potential users by providing a reliable, well-documented, source of seasonal climate time-scale ocean reanalysis to complement the atmospheric reanalyses available elsewhere (NOAA/EMC, NASA/GMAO, and ECMWF, for example). SODA3 (SODA Version 3) is the latest release of SODA. The model has been switched to GFDL MOM5/SIS1 with eddy permitting 0.25 degree by 0.25 degree by 50 level resolution (28 kilometers at the Equator down to less than 10 kilometers at polar latitudes), similar to the ocean component of the GFDL CM2.5 coupled climate model, and includes the same SIS1 active sea ice model. A number of improvements have been included in the sequential DA filter, but for many reanalyses SODA3 retains a pre-specified flow-dependent error covariance. One of the focuses for SODA3 has been to identify, quantify, and limit sources of bias. A major source of bias is in the forward model that predicts the evolution of the flow. A major (but not the only) source of model bias, in turn, is introduced through bias in the meteorological fluxes (heat, freshwater, and momentum). To address this problem SODA3 is an 'ensemble' reanalysis, the spread of whose members provides information about sensitivity to errors in surface forcing. Many of these ensemble members are driven by fluxes that have been bias-corrected.