Ricardo Todling

MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications

AbstractThe Modern-Era Retrospective Analysis for Research and Applications (MERRA) was undertaken by NASA’s Global Modeling and Assimilation Office with two primary objectives: to place observations from NASA’s Earth Observing System satellites into a climate context and to improve upon the hydrologic cycle represented in earlier generations of reanalyses. Focusing on the satellite era, from 1979 to the present, MERRA has achieved its goals with significant improvements in precipitation and water vapor climatology. Here, a brief overview of the system and some aspects of its performance, including quality assessment diagnostics from innovation and residual statistics, is given.By comparing MERRA with other updated reanalyses [the interim version of the next ECMWF Re-Analysis (ERA-Interim) and the Climate Forecast System Reanalysis (CFSR)], advances made in this new generation of reanalyses, as well as remaining deficiencies, are identified. Although there is little difference between the new reanalyses i...

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)

The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) is the latest atmospheric reanalysis of the modern satellite era produced by NASAs Global Modeling and Assimilation Office (GMAO). MERRA-2 assimilates observation types not available to its predecessor, MERRA, and includes updates to the Goddard Earth Observing System (GEOS) model and analysis scheme so as to provide a viable ongoing climate analysis beyond MERRAs terminus. While addressing known limitations of MERRA, MERRA-2 is also intended to be a development milestone for a future integrated Earth system analysis (IESA) currently under development at GMAO. This paper provides an overview of the MERRA-2 system and various performance metrics. Among the advances in MERRA-2 relevant to IESA are the assimilation of aerosol observations, several improvements to the representation of the stratosphere including ozone, and improved representations of cryospheric processes. Other improvements in the quality of MERRA-2 compared with MERRA include the reduction of some spurious trends and jumps related to changes in the observing system, and reduced biases and imbalances in aspects of the water cycle. Remaining deficiencies are also identified. Production of MERRA-2 began in June 2014 in four processing streams, and converged to a single near-real time stream in mid 2015. MERRA-2 products are accessible online through the NASA Goddard Earth Sciences Data Information Services Center (GES DISC).

Data Assimilation in the Presence of Forecast Bias: the GEOS Moisture Analysis

The authors describe the application of the unbiased sequential analysis algorithm developed by Dee and da Silva to the Goddard Earth Observing System moisture analysis. The algorithm estimates the slowly varying, systematic component of model error from rawinsonde observations and adjusts the first-guess moisture field accordingly. Results of two seasonal data assimilation cycles show that moisture analysis bias is almost completely eliminated in all observed regions. The improved analyses cause a sizable reduction in the 6-h forecast bias and a marginal improvement in the error standard deviations.

A fixed-lag Kalman smoother for retrospective data assimilation

Abstract Data assimilation has traditionally been employed to provide initial conditions for numerical weather prediction (NWP). A multiyear time sequence of objective analyses produced by data assimilation can also be used as an archival record from which to carry out a variety of atmospheric process studies. For this latter propose, NWP analyses are not as accurate as they could be, for each analysis is based only on current and past observed data, and not on any future data. Analyses incorporating future data, as well as current and past data, are termed retrospective analyses. The problem of retrospective objective analysis has not yet received attention in the meteorological literature. In this paper, the fixed-lag Kalman smoother (FLKS) is proposed as a means of providing retrospective analysis capability in data assimilation. The FLKS is a direct generalization of the Kalman filter. It incorporates all data observed up to and including some fixed amount of time past each analysis time. A computatio...

Suboptimal Schemes for Atmospheric Data Assimilation Based on the Kalman Filter

Abstract This work is directed toward approximating the evolution of forecast error covariances for data assimilation. The performance of different algorithms based on simplification of the standard Kalman filter (KF) is studied. These are suboptimal schemes (SOSs) when compared to the KF, which is optimal for linear problems with known statistics. The SOSs considered here are several versions of optimal interpolation (OI), a scheme for height error variance advection, and a simplified KF in which the full height error covariance is advected. To employ a methodology for exact comparison among these schemes, a linear environment is maintained, in which a beta-plane shallow-water model linearized about a constant zonal flow is chosen for the test-bed dynamics. The results show that constructing dynamically balanced forecast error covariances rather than using conventional geostrophically balanced ones is essential for successful performance of any SOS. A posteriori initialization of SOSs to compensate for m...

The THORPEX Observation Impact Intercomparison Experiment

Abstract An experiment is being conducted to directly compare the impact of all assimilated observations on short-range forecast errors in different forecast systems using an adjoint-based technique. The technique allows detailed comparison of observation impacts in terms of data type, location, satellite sounding channel, or other relevant attributes. This paper describes results for a “baseline” set of observations assimilated by three forecast systems for the month of January 2007. Despite differences in the assimilation algorithms and forecast models, the impacts of the major observation types are similar in each forecast system in a global sense. However, regional details and other aspects of the results can differ substantially. Large forecast error reductions are provided by satellite radiances, geostationary satellite winds, radiosondes, and commercial aircraft. Other observation types provide smaller impacts individually, but their combined impact is significant. Only a small majority of the tota...

An adaptive buddy check for observational quality control

An adaptive buddy-check algorithm is presented that adjusts tolerances for suspect observations, based on the variability of surrounding data. The algorithm derives from a statistical hypothesis test combined with maximum-likelihood covariance estimation. Its stability is shown to depend on the initial identification of outliers by a simple background check. The adaptive feature ensures that the final quality-control decisions are not very sensitive to prescribed statistics of first-guess and observation errors, nor on other approximations introduced into the algorithm. The implementation of the algorithm in a global atmospheric data assimilation is described. Its performance is contrasted with that of a non-adaptive buddy check, for the surface analysis of an extreme storm that took place over Europe on 27 December 1999. The adaptive algorithm allowed the inclusion of many important observations that differed greatly from the first guess and that would have been excluded on the basis of prescribed statistics. The analysis of the storm development was much improved as a result of these additional observations.

Maintaining Atmospheric Mass and Water Balance Within Reanalysis

This study describes the modifications made to the Goddard Earth Observing System (GEOS) Atmospheric Data Assimilation System (ADAS) to conserve atmospheric dry-air mass and to guarantee that the net source of water from precipitation and surface evaporation equals the change in total atmospheric water. The modifications involve changes to both the atmospheric model and the analysis procedure. In the model, sources and sinks of water are included in the continuity equation; in the analysis, constraints are imposed to penalize (and thus minimize) analysis increments of dry-air mass. Finally, changes are also required to the Incremental Analysis Update (IAU) procedure. The effects of these modifications are separately evaluated in free-running and assimilation experiments. Results are also presented from a multiyear reanalysis (Version 2 of the Modern Era Retrospective-Analysis for Research and Applications: MERRA-2) that uses the modified system.

Tracking Atmospheric Instabilities with the Kalman Filter. Part 1: Methodology and One-Layer Resultst

Abstract Sequential data assimilation schemes approaching true optimality for sizable atmospheric models are becoming a reality. The behavior of the Kalman filter (KF) under difficult conditions needs therefore to be understood. In this two-part paper we implement a KF for a two-dimensional shallow-water model, with one or two layers. The model is linearized about a basic flow that depends on latitude; this permits the one-layer (1-L) case to be barotropically unstable. Constant vertical shear in the two-layer (2-L) case induces baroclinic instability. A model-error covariance matrix for the KF simulations is constructed based on the hypothesis that an ensemble of slow modes dominates the errors. In the 1-L case, the system is stable for a meridionally constant basic flow. Assuming equipartition of energy in the construction of the model-error covariance matrix has a deleterious effect on the process of data assimilation in both the stable and unstable cases. Estimation errors are found to be smaller for ...

Suboptimal Schemes for Retrospective Data Assimilation Based on the Fixed-Lag Kalman Smoother

Abstract The fixed-lag Kalman smoother was proposed recently by S. E. Cohn et al. as a framework for providing retrospective data assimilation capability in atmospheric reanalysis projects. Retrospective data assimilation refers to the dynamically consistent incorporation of data observed well past each analysis time into each analysis. Like the Kalman filter, the fixed-lag Kalman smoother requires statistical information that is not available in practice and involves an excessive amount of computation if implemented by brute force, and must therefore be approximated sensibly to become feasible for operational use. In this article the performance of suboptimal retrospective data assimilation systems (RDASs) based on a variety of approximations to the optimal fixed-lag Kalman smoother is evaluated. Since the fixed-lag Kalman smoother formulation employed in this work separates naturally into a (Kalman) filter portion and an optimal retrospective analysis portion, two suboptimal strategies are considered: (...