Virtual Event: ECMWF/EUMETSAT NWP SAF Workshop on the treatment of random and systematic errors in satellite data assimilation for NWP
Session
Machine learning (ML) is poised to play a key role in the next generation of forecast models for the Earth system. One specific approach of interest is hybrid modeling, where a ML model is trained to represent systematic (predictable) portion of the forecast error for traditional physics-based models. Then the ML model is used as an augmented tendency term in the traditional physics-based...
Recent studies have shown that some high-resolution remote-sensing observations exhibit spatial error-correlations, so the observation error covariance matrix should not be a diagonal matrix, but a dense matrix with nonzero off-diagonal entries. In this case, the multiplication of the inverse of this matrix with a vector in the solution of the variational minimization problem may be much more...
Spire Global develops and maintains its own in-house ionospheric data assimilation (DA) model called STEAM. STEAM assimilates ground and space-based Slant Total Electron Content (STEC) observations generated from global navigation satellite system (GNSS) signals and combines these with a background model to produce estimates of the 3D electron density field. The production of the STEC...
Characterizing and modeling of cloud dependent observation error covariances are crucial to all-sky infrared radiance assimilation. Our previous studies demonstrated a cloud dependent observation error model using a symmetric cloud effect parameter performed well to assimilate all-sky infrared radiances of Himawari-8. We developed a diagonal observation error covariance that was inflated with...
Currently infrared (IR) sounder data is used in clear scenes only. This includes completely clear scenes, and in addition channels which are considered to be unaffected by the cloud above a cloudy scene. Completely clear scenes represent approximately only 10% of the data. Thus, majority of the hyperspectral data used actively in assimilation are from cloudy situations and potentially affected...
In atmospheric chemistry retrievals and data assimilation systems, observation errors associated with satellite radiances are chosen empirically and generally treated as uncorrelated. In this work, we estimate inter-channel error covariances for the Infrared Atmospheric Sounding Interferometer (IASI) and evaluate their impact on ozone assimilation with the chemical transport model MOCAGE....
Current observations used in numerical weather prediction systems come mostly from spaceborne thermal infrared sounders such as AIRS, IASI and CrIS. However, the thermal infrared only constitutes half of the Earth's emitted radiance, the other half being the far infrared (far-IR), ranging from 15 to 100 μm. In recent years, some theoretical studies have shown the added-value of far-IR...
In the modern era satellite data have increasingly become the dominant source of information assimilated in Numerical Weather Prediction (NWP) models. Before 1979, however, very little satellite data is currently assimilated into climate reanalyses. For example, ERA5 only uses one pre-1979 sensor (the VTPR instrument flown on the NOAA series of satellites) even though other sensors are...
Many observations, especially from satellites have biases: operationally, a variational bias correction (VarBC) is often used to correct these biases. VarBC assumes that all error covariances have been specified correctly, but this is often not the case. The impact of mis-specifying the background error covariance has previously been explored in a system without bias correction by Eyre and...
Recent developments in numerical weather prediction have led to the use of correlated observation‐error covariance (OEC) information in data assimilation and forecasting systems. However, diagnosed OEC matrices are often ill‐conditioned and may cause convergence problems for variational data assimilation procedures. Reconditioning methods are used to improve the conditioning of covariance...
The presented work will illustrate the impact of analysis correction-based additive inflation (ACAI) applied to the global atmosphere in an uncoupled (NAVGEM) and coupled (Navy ESPC) model. ACAI uses analysis corrections from the NAVDAS-AR data assimilation system as a representation of model error. The seasonal mean analysis corrections are shown to match well with observational estimates of...
We have developed a Variational Bias Correction scheme (VarBC) for estimating and correcting observation bias in satellite measured Sea Surface Temperature (SST), Sea Surface Height (SSH), and Sea Surface Salinity (SSS). In our methodology, VarBC has been implemented by updating our 3DVar data assimilation scheme so that it can estimate both the model state and observation biases...