Comparing Two Approaches for Assessing Observation Impact

Langland and Baker introduced an approach to assess the impact of observations on the forecasts. In that approach, a state-space aspect of the forecast is defined and a procedure is derived ultimately relating changes in the aspect with changes in the observing system. Some features of the state-space approach are to be noted: the typical choice of forecast aspect is rather subjective and leads to incomplete assessment of the observing system, it requires availability of a verification state that is in practice correlated with the forecast, and it involves the adjoint operator of the entire data assimilation system and is thus constrained by the validity of this operator. This article revisits the topic of observation impacts from the perspective of estimation theory. An observation-space metric is used to allow inferring observation impact on the forecasts without the limitations just mentioned. Using differences of observation-minus-forecast residuals obtained from consecutive forecasts leads to the following advantages: (i) it suggests a rather natural choice of forecast aspect that directly links to the data assimilation procedure, (ii) it avoids introducing undesirable correlations in the forecast aspect since verification is done against the observations, and (iii) it does not involve linearization and use of adjoints. The observation-space approach has the additional advantage of being nearly cost free and very simple to implement. In its simplest form it reduces to evaluating the statistics of observationminus- background and observation-minus-analysis residuals with traditional methods. Illustrations comparing the approaches are given using the NASA Goddard Earth Observing System

Introducing Object-Oriented Concepts into GSI

Enhancements are now being made to the Gridpoint Statistical Interpolation (GSI) data assimilation system to expand its capabilities. This effort opens the way for broadening the scope of GSI's applications by using some standard object-oriented features in Fortran, and represents a starting point for the so-called GSI refactoring, as a part of the Joint Effort for Data-assimilationI ntegration (JEDI) project of JCSDA

The GMAO Hybrid Ensemble-Variational Atmospheric Data Assimilation System: Version 2.0

This document describes the implementation and usage of the Goddard Earth Observing System (GEOS) Hybrid Ensemble-Variational Atmospheric Data Assimilation System (Hybrid EVADAS). Its aim is to provide comprehensive guidance to users of GEOS ADAS interested in experimenting with its hybrid functionalities. The document is also aimed at providing a short summary of the state-of-science in this release of the hybrid system. As explained here, the ensemble data assimilation system (EnADAS) mechanism added to GEOS ADAS to enable hybrid data assimilation applications has been introduced to the pre-existing machinery of GEOS in the most non-intrusive possible way. Only very minor changes have been made to the original scripts controlling GEOS ADAS with the objective of facilitating its usage by both researchers and the GMAO's near-real-time Forward Processing applications. In a hybrid scenario two data assimilation systems run concurrently in a two-way feedback mode such that: the ensemble provides background ensemble perturbations required by the ADAS deterministic (typically high resolution) hybrid analysis; and the deterministic ADAS provides analysis information for recentering of the EnADAS analyses and information necessary to ensure that observation bias correction procedures are consistent between both the deterministic ADAS and the EnADAS. The nonintrusive approach to introducing hybrid capability to GEOS ADAS means, in particular, that previously existing features continue to be available. Thus, not only is this upgraded version of GEOS ADAS capable of supporting new applications such as Hybrid 3D-Var, 3D-EnVar, 4D-EnVar and Hybrid 4D-EnVar, it remains possible to use GEOS ADAS in its traditional 3D-Var mode which has been used in both MERRA and MERRA-2. Furthermore, as described in this document, GEOS ADAS also supports a configuration for exercising a purely ensemble-based assimilation strategy which can be fully decoupled from its variational component. We should point out that Release 1.0 of this document was made available to GMAO in mid-2013, when we introduced Hybrid 3D-Var capability to GEOS ADAS. This initial version of the documentation included a considerably different state-of-science introductory section but many of the same detailed description of the mechanisms of GEOS EnADAS. We are glad to report that a few of the desirable Future Works listed in Release 1.0 have now been added to the present version of GEOS EnADAS. These include the ability to exercise an Ensemble Prediction System that uses the ensemble analyses of GEOS EnADAS and (a very early, but functional version of) a tool to support Ensemble Forecast Sensitivity and Observation Impact applications

Hybrid Data Assimilation without Ensemble Filtering

The Global Modeling and Assimilation Office is preparing to upgrade its three-dimensional variational system to a hybrid approach in which the ensemble is generated using a square-root ensemble Kalman filter (EnKF) and the variational problem is solved using the Grid-point Statistical Interpolation system. As in most EnKF applications, we found it necessary to employ a combination of multiplicative and additive inflations, to compensate for sampling and modeling errors, respectively and, to maintain the small-member ensemble solution close to the variational solution; we also found it necessary to re-center the members of the ensemble about the variational analysis. During tuning of the filter we have found re-centering and additive inflation to play a considerably larger role than expected, particularly in a dual-resolution context when the variational analysis is ran at larger resolution than the ensemble. This led us to consider a hybrid strategy in which the members of the ensemble are generated by simply converting the variational analysis to the resolution of the ensemble and applying additive inflation, thus bypassing the EnKF. Comparisons of this, so-called, filter-free hybrid procedure with an EnKF-based hybrid procedure and a control non-hybrid, traditional, scheme show both hybrid strategies to provide equally significant improvement over the control; more interestingly, the filter-free procedure was found to give qualitatively similar results to the EnKF-based procedure

Atmosphere-Ocean Coupled Data Assimilation Using NASA GEOS: Estimation of Air-Sea Interface State Variables

Air-sea interface variables, such as the skin Sea Surface Temperature (SST) are essential for atmosphere-ocean coupling. In the NASA GMAO Data Assimilation System (DAS), the skin SST and 3-D atmospheric state are jointly estimated [1]. This presentation is focused on the prior or background error covariance that is used in this analysis. The GEOS DAS uses an ensemble-variational assimilation strategy. In that, specification of a climatological background (CB) error covariance for SST relies on the NOAA's OI SST, with estimates of standard deviation and correlation length scales based on weekly analyses of the bulk SST at 1 degree resolution. However, present analysis system is striving to resolve SST diurnal variability with six hourly analyses and assimilates a vast number of in-situ and satellite observations. The first part of this presentation re-derives the CB error covariance using OSTIA SST analyses and illustrates the impact of this update on assimilating satellite observations. In a hybrid assimilation system the CB error covariances are appended with a flow-dependent background error covariance estimate implied by the underlying ensemble. The second part of this presentation refers to: a. treatment of the skin SST in the ensemble members, b. corresponding ensemble spread, and c. impact of these additions on the data assimilation system. [1] S. Akella, et al. (2017), doi:10.1002/qj.298

Estimating Model Error Using Observation Residuals

This presentation discusses an approach to estimate model error using observation residuals. Based on the sequential fixed-lag smoother; we introduce a diagnostic procedure to allow estimating model error over a dense observing system. Optimality considerations are examined in light of the sequential results. The procedure is re-interpreted in the language of variational assimilation, such as 4d-Var. Illustrations of the approach are given by studying both identical-twin and fraternal-twin experimental settings for a system governed by Lorenz-type dynamics. Preliminary results by looking at observation residual statistics for the ECMWF data assimilation system are also shown. The presentation will be part of a series of discussions on issues related to four-dimensional data assimilation under weak-constraint and methodologies to estimate model error

Near-Real Time Ocean-Atmosphere Skin Temperature as Part of NASA GMAO Atmospheric Data Assimilation System

The Global Modeling and Assimilation Office (GMAO) at NASA, GSFC is developing an integrated Earth system analysis (IESA). Integral to this IESA are ocean-atmosphere interface states, such as the skin SST. Recently the GMAO's near-real time operational weather analysis and prediction system implemented an analysis for skin SST along with the meteorological analysis since Jan, 2017. The skin SST is modeled and constrained using infrared radiometric observations. This poster describes some of the details of this development, its impact on forecasts, current and future developments towards the inclusion of microwave data (e.g. GPM-GMI)

Assimilation for Skin SST in the NASA GEOS Atmospheric Data Assimilation System

The present article describes the sea surface temperature (SST) developments implemented in the Goddard Earth Observing System, Version 5 (GEOS) Atmospheric Data Assimilation System (ADAS). These are enhancements that contribute to the development of an atmosphere-ocean coupled data assimilation system using GEOS. In the current quasi-operational GEOS-ADAS, the SST is a boundary condition prescribed based on the OSTIA product, therefore SST and skin SST (Ts) are identical. This work modifies the GEOS-ADAS Ts by modelling and assimilating near sea surface sensitive satellite infrared (IR) observations. The atmosphere-ocean interface layer of the GEOS atmospheric general circulation model (AGCM) is updated to include near-surface diurnal warming and cool-skin effects. The GEOS analysis system is also updated to directly assimilate SST-relevant Advanced Very High Resolution Radiometer (AVHRR) radiance observations. Data assimilation experiments designed to evaluate the Ts modification in GEOS-ADAS show improvements in the assimilation of radiance observations that extend beyond the thermal infrared bands of AVHRR. In particular, many channels of hyperspectral sensors, such as those of the Atmospheric Infrared Sounder (AIRS), and Infrared Atmospheric Sounding Interferometer (IASI) are also better assimilated. We also obtained improved fit to withheld insitu buoy measurement of near-surface SST. Evaluation of forecast skill scores show neutral to marginal benefit from the modified Ts

Maintaining Atmospheric Mass and Water Balance Within Reanalysis

This report describes the modifications implemented into the Goddard Earth Observing System Version-5 (GEOS-5) Atmospheric Data Assimilation System (ADAS) to maintain global conservation of dry atmospheric mass as well as to preserve the model balance of globally integrated precipitation and surface evaporation during reanalysis. Section 1 begins with a review of these global quantities from four current reanalysis efforts. Section 2 introduces the modifications necessary to preserve these constraints within the atmospheric general circulation model (AGCM), the Gridpoint Statistical Interpolation (GSI) analysis procedure, and the Incremental Analysis Update (IAU) algorithm. Section 3 presents experiments quantifying the impact of the new procedure. Section 4 shows preliminary results from its use within the GMAO MERRA-2 Reanalysis project. Section 5 concludes with a summary