scholarly journals NWP verification against own analysis by using a Data Assimilation confidence mask

2021 ◽  
Author(s):  
Barbara Casati ◽  
Vincent Fortin ◽  
Franck Lespinas ◽  
Dikraa Khedhaouiria
Keyword(s):  
Data Assimilation ◽  
Background Model ◽  
Polar Regions ◽  
Error Statistics ◽  
Station Network ◽  
Satellite Retrieval ◽  
Surface Network ◽  
Precipitation Analysis ◽  
Grid Points ◽  
Full Domain

<p>Numerical Model Prediction (NWP) verification against station measurements from a surface network is affected by sub-tile representativeness issues. Moreover, the station network is often not representative of the whole verification domain (e.g. usually coastal stations are predominant) and large unpopulated regions (such as oceans, Polar regions, deserts) are under-sampled. Verification against gridded analyses mitigate these issues, since they partially address the sub-tile representativeness, and sample homogeneously the verification domain. Moreover, gridded analyses merge station network measurements to radar and satellite retrieval estimates, in a physical coherent fashion, over the same NWP grid. Verification against own analysis, despite quite convenient, is however hampered by its dependence on the NWP background model, which renders the verification “incestuous”, further than being affected by the uncertainties introduced by retrieval algorithms and Data Assimilation (DA) procedures.</p><p>In this study we investigate the use of a gridded NWP own analysis for verification, by applying a mask to reduce the background model contribution. The mask weights the verification scores to account for the amounts of observations assimilated and their associated uncertainty, as estimated from DA. We illustrate the approach by using the Canadian Precipitation Analysis (CaPA), which assimilates station measurements, radar and satellite-based (IMERG) observations. The CaPA confidence (weighting) mask is dynamic and changes depending on the daily available (assimilated) observations, and on their corresponding DA error statistics; it is defined as</p><p>                                             mask = 1 - var(A-O)/var(B-O)</p><p>where A=analysis, B=Background, O=observations. Where the analysis is identical to the background model, the weighting mask is zero.</p><p>We evaluate the Canadian Regional Deterministic Prediction System (RDPS), which is the NWP system used as background model for CaPA. As expected, the verification results obtained by using the weighting mask lay between the verification results obtained verifying against the analysis over the full domain, and the results obtained verifying against station measurements. The effects of sub-tile representativeness are quantified by comparing verification results against station measurements to verification results against CaPA for the grid-points co-located with the stations. Finally, the comparison of the verification results against CaPA over the full domain versus the verification results against CaPA for the grid-points co-located with stations, estimates to which extent the station network is representative of the full domain.</p><p>The approach aims to propose a simple -yet effective- better practice for verification against own analysis.</p>

scholarly journals Multivariate Error Covariance Estimates by Monte Carlo Simulation for Assimilation Studies in the Pacific Ocean

2005 ◽  
Vol 133 (8) ◽  
pp. 2310-2334 ◽  
Author(s):  
Anna Borovikov ◽  
Michele M. Rienecker ◽  
Christian L. Keppenne ◽  
Gregory C. Johnson
Keyword(s):  
Monte Carlo ◽  
Data Assimilation ◽  
Forecast Error ◽  
Independent Set ◽  
Velocity Fields ◽  
Optimal Interpolation ◽  
Model Errors ◽  
Error Statistics ◽  
Practical Applications ◽  
Monte Carlo Techniques

Abstract One of the most difficult aspects of ocean-state estimation is the prescription of the model forecast error covariances. The paucity of ocean observations limits our ability to estimate the covariance structures from model–observation differences. In most practical applications, simple covariances are usually prescribed. Rarely are cross covariances between different model variables used. Here a comparison is made between a univariate optimal interpolation (UOI) scheme and a multivariate OI algorithm (MvOI) in the assimilation of ocean temperature profiles. In the UOI case only temperature is updated using a Gaussian covariance function. In the MvOI, salinity, zonal, and meridional velocities as well as temperature are updated using an empirically estimated multivariate covariance matrix. Earlier studies have shown that a univariate OI has a detrimental effect on the salinity and velocity fields of the model. Apparently, in a sequential framework it is important to analyze temperature and salinity together. For the MvOI an estimate of the forecast error statistics is made by Monte Carlo techniques from an ensemble of model forecasts. An important advantage of using an ensemble of ocean states is that it provides a natural way to estimate cross covariances between the fields of different physical variables constituting the model-state vector, at the same time incorporating the model’s dynamical and thermodynamical constraints as well as the effects of physical boundaries. Only temperature observations from the Tropical Atmosphere–Ocean array have been assimilated in this study. To investigate the efficacy of the multivariate scheme, two data assimilation experiments are validated with a large independent set of recently published subsurface observations of salinity, zonal velocity, and temperature. For reference, a control run with no data assimilation is used to check how the data assimilation affects systematic model errors. While the performance of the UOI and MvOI is similar with respect to the temperature field, the salinity and velocity fields are greatly improved when the multivariate correction is used, as is evident from the analyses of the rms differences between these fields and independent observations. The MvOI assimilation is found to improve upon the control run in generating water masses with properties close to the observed, while the UOI fails to maintain the temperature and salinity structure.

scholarly journals Interpretation of Adaptive Observing Guidance for Atlantic Tropical Cyclones

2007 ◽  
Vol 135 (12) ◽  
pp. 4006-4029 ◽  
Author(s):  
C. A. Reynolds ◽  
M. S. Peng ◽  
S. J. Majumdar ◽  
S. D. Aberson ◽  
C. H. Bishop ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Total Energy ◽  
Tropical Cyclones ◽  
Error Variance ◽  
Naval Research ◽  
Error Statistics ◽  
Background Error ◽  
Analysis Error ◽  
The Impact ◽  
Perturbation Growth

Abstract Adaptive observing guidance products for Atlantic tropical cyclones are compared using composite techniques that allow one to quantitatively examine differences in the spatial structures of the guidance maps and relate these differences to the constraints and approximations of the respective techniques. The guidance maps are produced using the ensemble transform Kalman filter (ETKF) based on ensembles from the National Centers for Environmental Prediction and the European Centre for Medium-Range Weather Forecasts (ECMWF), and total-energy singular vectors (TESVs) produced by ECMWF and the Naval Research Laboratory. Systematic structural differences in the guidance products are linked to the fact that TESVs consider the dynamics of perturbation growth only, while the ETKF combines information on perturbation evolution with error statistics from an ensemble-based data assimilation scheme. The impact of constraining the SVs using different estimates of analysis error variance instead of a total-energy norm, in effect bringing the two methods closer together, is also assessed. When the targets are close to the storm, the TESV products are a maximum in an annulus around the storm, whereas the ETKF products are a maximum at the storm location itself. When the targets are remote from the storm, the TESVs almost always indicate targets northwest of the storm, whereas the ETKF targets are more scattered relative to the storm location and often occur over the northern North Atlantic. The ETKF guidance often coincides with locations in which the ensemble-based analysis error variance is large. As the TESV method is not designed to consider spatial differences in the likely analysis errors, it will produce targets over well-observed regions, such as the continental United States. Constraining the SV calculation using analysis error variance values from an operational 3D variational data assimilation system (with stationary, quasi-isotropic background error statistics) results in a modest modulation of the target areas away from the well-observed regions, and a modest reduction of perturbation growth. Constraining the SVs using the ETKF estimate of analysis error variance produces SV targets similar to ETKF targets and results in a significant reduction in perturbation growth, due to the highly localized nature of the analysis error variance estimates. These results illustrate the strong sensitivity of SVs to the norm (and to the analysis error variance estimate used to define it) and confirm that discrepancies between target areas computed using different methods reflect the mathematical and physical differences between the methods themselves.

scholarly journals First steps towards the assimilation of IASI ozone data into the MOCAGE-PALM system

2009 ◽  
Vol 9 (2) ◽  
pp. 6691-6737 ◽  
Author(s):  
S. Massart ◽  
C. Clerbaux ◽  
D. Cariolle ◽  
A. Piacentini ◽  
S. Turquety ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Total Ozone ◽  
Transport Model ◽  
Polar Regions ◽  
Observation Error ◽  
Random Errors ◽  
Chemistry Transport Model ◽  
Ozone Data ◽  
The One ◽  
New Generation

Abstract. The Infrared Atmospheric Sounding Interferometer (IASI) is one of the five European new generation instruments carried by the polar-orbiting MetOp-A satellite. Data assimilation is a powerful tool to combine these data with a numerical model. This paper presents the first steps made towards the assimilation of the total ozone columns from the IASI measurements into a chemistry transport model. The IASI ozone data used are provided by an inversion of radiances performed at the LATMOS (Laboratoire Atmosphères, Milieux, Observations Spatiales). As a contribution to the validation of this dataset, the LATMOS-IASI data are compared to a four dimensional ozone field, with low systematic and random errors compared to ozonesondes and OMI-DOAS data. This field results from the combined assimilation of ozone profiles from the MLS instrument and of total ozone columns from the SCIAMACHY instrument. It is found that on average, the LATMOS-IASI data tends to overestimate the total ozone columns by 2% to 8%. The random observation error of the LATMOS-IASI data is estimated to about 6%, except over polar regions and deserts where it is higher. Using this information, the LATMOS-IASI data are then assimilated, combined with the MLS data. This first LATMOS-IASI data assimilation experiment shows that the resulting analysis is quite similar to the one obtained from the combined MLS and SCIAMACHY data assimilation.

scholarly journals High-Resolution (2.5 km) Ensemble Precipitation Analysis across Canada

2020 ◽  
Vol 21 (9) ◽  
pp. 2023-2039
Author(s):  
Dikra Khedhaouiria ◽  
Stéphane Bélair ◽  
Vincent Fortin ◽  
Guy Roy ◽  
Franck Lespinas
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Background Field ◽  
Spatiotemporal Resolution ◽  
Stochastic Perturbations ◽  
Multiple Observations ◽  
Uncertainty Estimates ◽  
Precipitation Analysis ◽  
Data Assimilation System ◽  
Assimilation System

AbstractConsistent and continuous fields provided by precipitation analyses are valuable for hydrometeorological applications and land data assimilation modeling, among others. Providing uncertainty estimates is a logical step in the analysis development, and a consistent approach to reach this objective is the production of an ensemble analysis. In the present study, a 6-h High-Resolution Ensemble Precipitation Analysis (HREPA) was developed for the domain covering Canada and the northern part of the contiguous United States. The data assimilation system is the same as the Canadian Precipitation Analysis (CaPA) and is based on optimal interpolation (OI). Precipitation from the Canadian national 2.5-km atmospheric prediction system constitutes the background field of the analysis, while at-site records and radar quantitative precipitation estimates (QPE) compose the observation datasets. By using stochastic perturbations, multiple observations and background field random realizations were generated to subsequently feed the data assimilation system and provide 24 HREPA members plus one control run. Based on one summer and one winter experiment, HREPA capabilities in terms of bias and skill were verified against at-site observations for different climatic regions. The results indicated HREPA’s reliability and skill for almost all types of precipitation events in winter, and for precipitation of medium intensity in summer. For both seasons, HREPA displayed resolution and sharpness. The overall good performance of HREPA and the lack of ensemble precipitation analysis (PA) at such spatiotemporal resolution in the literature motivate further investigations on transitional seasons and more advanced perturbation approaches.

scholarly journals Simulation of Monsoon Depressions Using WRF-VAR: Impact of Different Background Error Statistics and Lateral Boundary Conditions

2014 ◽  
Vol 142 (10) ◽  
pp. 3586-3613 ◽  
Author(s):  
A. Routray ◽  
S. C. Kar ◽  
P. Mali ◽  
K. Sowjanya
Keyword(s):  
Data Assimilation ◽  
Boundary Conditions ◽  
Initial Conditions ◽  
Variational Data Assimilation ◽  
Error Statistics ◽  
Background Error ◽  
Single Observation ◽  
Monsoon Depressions ◽  
Data Assimilation System ◽  
Assimilation System

Abstract In a variational data assimilation system, background error statistics (BES) spread the influence of the observations in space and filter analysis increments through dynamic balance or statistical relationships. In a data-sparse region such as the Bay of Bengal, BES play an important role in defining the location and structure of monsoon depressions (MDs). In this study, the Indian-region-specific BES have been computed for the Weather Research and Forecasting (WRF) three-dimensional variational data assimilation system. A comparative study using single observation tests is carried out using the computed BES and global BES within the WRF system. Both sets of BES are used in the assimilation cycles and forecast runs for simulating the meteorological features associated with the MDs. Numerical experiments have been conducted to assess the relative impact of various BES in the analysis and simulations of the MDs. The results show that use of regional BES in the assimilation cycle has a positive impact on the prediction of the location, propagation, and development of rainbands associated with the MDs. The track errors of MDs are smaller when domain-specific BES are used in the assimilation cycle. Additional experiments have been conducted using data from the Interim European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-Interim) as initial and boundary conditions (IBCs) in the assimilation cycle. The results indicate that the use of domain-dependent BES and high-resolution ERA-I data as IBCs further improved the initial conditions for the model leading to better forecasts of the MDs.

scholarly journals Multiscale Representation of Observation Error Statistics in Data Assimilation

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1460
Author(s):  
Vincent Chabot ◽  
Maëlle Nodet ◽  
Arthur Vidard
Keyword(s):  
Data Assimilation ◽  
Real Life ◽  
Covariance Matrices ◽  
Multiscale Modelling ◽  
Observation Error ◽  
Error Statistics ◽  
Life Data ◽  
Promising Tool ◽  
Error Covariance ◽  
Real Life Data

Accounting for realistic observation errors is a known bottleneck in data assimilation, because dealing with error correlations is complex. Following a previous study on this subject, we propose to use multiscale modelling, more precisely wavelet transform, to address this question. This study aims to investigate the problem further by addressing two issues arising in real-life data assimilation: how to deal with partially missing data (e.g., concealed by an obstacle between the sensor and the observed system), and how to solve convergence issues associated with complex observation error covariance matrices? Two adjustments relying on wavelets modelling are proposed to deal with those, and offer significant improvements. The first one consists of adjusting the variance coefficients in the frequency domain to account for masked information. The second one consists of a gradual assimilation of frequencies. Both of these fully rely on the multiscale properties associated with wavelet covariance modelling. Numerical results on twin experiments show that multiscale modelling is a promising tool to account for correlations in observation errors in realistic applications.

scholarly journals Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project

Atmosphere ◽  
2019 ◽  
Vol 10 (3) ◽  
pp. 125 ◽  
Author(s):  
Sarah Dance ◽  
Susan Ballard ◽  
Ross Bannister ◽  
Peter Clark ◽  
Hannah Cloke ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Observation Error ◽  
Error Statistics ◽  
Convective Rainfall ◽  
Background Error ◽  
Spatially Correlated ◽  
Operational Forecasts ◽  
The Impact

The FRANC project (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) has researched improvements in numerical weather prediction of convective rainfall via the reduction of initial condition uncertainty. This article provides an overview of the project’s achievements. We highlight new radar techniques: correcting for attenuation of the radar return; correction for beams that are over 90% blocked by trees or towers close to the radar; and direct assimilation of radar reflectivity and refractivity. We discuss the treatment of uncertainty in data assimilation: new methods for estimation of observation uncertainties with novel applications to Doppler radar winds, Atmospheric Motion Vectors, and satellite radiances; a new algorithm for implementation of spatially-correlated observation error statistics in operational data assimilation; and innovative treatment of moist processes in the background error covariance model. We present results indicating a link between the spatial predictability of convection and convective regimes, with potential to allow improved forecast interpretation. The research was carried out as a partnership between University researchers and the Met Office (UK). We discuss the benefits of this approach and the impact of our research, which has helped to improve operational forecasts for convective rainfall events.

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