scholarly journals Assimilation of Wind Profiler Data in the Canadian Meteorological Centre’s Analysis Systems

2005 ◽  
Vol 22 (8) ◽  
pp. 1181-1194 ◽  
Author(s):  
Judy S. St-James ◽  
Stéphane Laroche
Keyword(s):  
Data Assimilation ◽  
Winter Season ◽  
Variational Data Assimilation ◽  
Radiosonde Data ◽  
Horizontal Wind ◽  
Observation Error ◽  
The Impact ◽  
Thinning Process ◽  
Vertical Correlation ◽  
Profiler Data

Abstract Real-time horizontal wind observations from the National Oceanic and Atmospheric Administration’s (NOAA’s) Profiler Network (NPN) are assessed in preparation for their assimilation in the Canadian Meteorological Centre (CMC) analysis systems. As a first step, radiosonde winds from 20 stations were compared to the central U.S. profiler stations over the 2001/02 winter season. It was found that profilers are at least as good as conventional radiosonde data. The 2001/02 winter season data were also used to examine the vertical correlation structure of the observation error for profilers. Using a statistical analysis of innovations, the observation error standard deviation of the wind components is estimated as 2.2 m s−1 and the vertical correlation length is approximately 500 m. These results suggest that the data are vertically correlated because they are available every 250 m. Therefore, a thinning process is proposed in which one out of three data are selected in the vertical for each station. Since January 2004, a close monitoring of NPN profiler data revealed significant errors at some stations in the lower and upper troposphere. Consequently, a monthly blacklist of NPN profilers is built based on data from the previous month. A data impact study with both the three-dimensional variational data assimilation (3DVAR) and four-dimensional variational data assimilation (4DVAR) analysis systems was conducted using data from the 2003/04 winter season in which the vertical thinning was tested. It was found that the vertical thinning improves slightly the 6-h forecast error, especially in the 4DVAR over the central United States in which 6 times more profilers are assimilated. The impact of the vertical thinning is found to be neutral in the 3DVAR. Also, the impact of profiler data is significant over the central U.S. domain compared to a control run with the only difference being the addition of profiler data. These results were sufficiently good to implement NPN profilers in both the CMC global and regional analysis systems with the thinning process in fall of 2004.

A New Approach for Estimating the Observation Impact in Ensemble–Variational Data Assimilation

2018 ◽  
Vol 146 (2) ◽  
pp. 447-465 ◽  
Author(s):  
Mark Buehner ◽  
Ping Du ◽  
Joël Bédard
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Forecast Model ◽  
Variational Data Assimilation ◽  
Observation Error ◽  
New Approach ◽  
Regional Prediction ◽  
Observation Impact ◽  
Adjoint Approach ◽  
The Impact

Abstract Two types of approaches are commonly used for estimating the impact of arbitrary subsets of observations on short-range forecast error. The first was developed for variational data assimilation systems and requires the adjoint of the forecast model. Comparable approaches were developed for use with the ensemble Kalman filter and rely on ensembles of forecasts. In this study, a new approach for computing observation impact is proposed for ensemble–variational data assimilation (EnVar). Like standard adjoint approaches, the adjoint of the data assimilation procedure is implemented through the iterative minimization of a modified cost function. However, like ensemble approaches, the adjoint of the forecast step is obtained by using an ensemble of forecasts. Numerical experiments were performed to compare the new approach with the standard adjoint approach in the context of operational deterministic NWP. Generally similar results are obtained with both approaches, especially when the new approach uses covariance localization that is horizontally advected between analysis and forecast times. However, large differences in estimated impacts are obtained for some surface observations. Vertical propagation of the observation impact is noticeably restricted with the new approach because of vertical covariance localization. The new approach is used to evaluate changes in observation impact as a result of the use of interchannel observation error correlations for radiance observations. The estimated observation impact in similarly configured global and regional prediction systems is also compared. Overall, the new approach should provide useful estimates of observation impact for data assimilation systems based on EnVar when an adjoint model is not available.

scholarly journals Impact of Radiosonde Balloon Drift on Numerical Weather Prediction and Verification

2013 ◽  
Vol 28 (3) ◽  
pp. 772-782 ◽  
Author(s):  
Stéphane Laroche ◽  
Réal Sarrazin
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Radiosonde Data ◽  
Horizontal Wind ◽  
Numerical Weather ◽  
Ascent Time ◽  
Verification Systems ◽  
The Impact ◽  
Range Forecasts

Abstract Radiosonde observations employed in real-time numerical weather prediction (NWP) applications are disseminated through the Global Telecommunication System (GTS) using alphanumeric codes. These codes do not include information about the position and elapsed ascent time of the balloon. Consequently, the horizontal balloon drift has generally been either ignored or estimated in data assimilation systems for NWP. With the increasing resolution of atmospheric models, it is now important to consider the positions and times of radiosonde data in both data assimilation and forecast verification systems. This information is now available in the Binary Universal Form for the Representation of Meteorological Data (BUFR) code for radiosonde data. This latter code will progressively replace the alphanumeric codes for all radiosonde data transmitted on the GTS. As a result, a strategy should be adopted by NWP centers to deal with the various codes for radiosonde data during this transition. In this work, a method for estimating the balloon drift position from reported horizontal wind components and a representative elapsed ascent time profile are developed and tested. This allows for estimating the missing positions and times information of radiosonde data in alphanumeric reports, and then for processing them like those available in BUFR code. The impact of neglecting the balloon position in data assimilation and verification systems is shown to be significant in short-range forecasts in the upper troposphere and stratosphere, especially for the zonal wind field in the Northern Hemisphere winter season. Medium-range forecasts are also improved overall when the horizontal position of radiosonde data is retrieved.

One-Dimensional Variational Data Assimilation of SSM/I Observations in Rainy Atmospheres at MSC

2007 ◽  
Vol 135 (1) ◽  
pp. 152-172 ◽  
Author(s):  
G. Deblonde ◽  
J-F. Mahfouf ◽  
B. Bilodeau ◽  
D. Anselmo
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Variational Data Assimilation ◽  
Scale Model ◽  
Observation Error ◽  
Step Method ◽  
One Dimensional ◽  
Brightness Temperatures ◽  
The Impact ◽  
Assimilation System

Abstract Currently, satellite radiances in the Canadian Meteorological Centre operational data assimilation system are only assimilated in clear skies. A two-step method, developed at the European Centre for Medium-Range Weather Forecasts, is considered to assimilate Special Sensor Microwave Imager (SSM/I) observations in rainy atmospheres. The first step consists of a one-dimensional variational data assimilation (1DVAR) method. Model temperature and humidity profiles are adjusted by assimilating either SSM/I brightness temperatures or retrieved surface rain rates (derived from SSM/I brightness temperatures). In the second step, 1DVAR column-integrated water vapor analyses are assimilated in four-dimensional variational data assimilation (4DVAR). At the Meteorological Service of Canada, such a 1DVAR assimilation system has been developed. Model profiles are obtained from a research version of the Global Environmental Multi-Scale model. Several issues raised while developing the 1DVAR system are addressed. The impact of the size of the observation error is studied when brightness temperatures are assimilated. For two case studies, analyses are derived when either surface rain rate or brightness temperatures are assimilated. Differences in the analyzed fields between these configurations are discussed and shortcomings of each approach are identified. Results of sensitivity studies are also provided. First the impact of observation error correlation between channels is investigated. Second, the size of the background temperature error is varied to assess its impact on the analyzed column-integrated water vapor. Third, the importance of each moist physical scheme is investigated. Finally, the portability of moist physical schemes specifically developed for data assimilation is discussed.

scholarly journals Accounting for Correlated Observation Error in a Dual-Formulation 4D Variational Data Assimilation System

2017 ◽  
Vol 145 (3) ◽  
pp. 1019-1032 ◽  
Author(s):  
William F. Campbell ◽  
Elizabeth A. Satterfield ◽  
Benjamin Ruston ◽  
Nancy L. Baker
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Error Variance ◽  
Variational Data Assimilation ◽  
Advanced Technology ◽  
Radiosonde Data ◽  
Observation Error ◽  
Error Covariance ◽  
Data Assimilation System ◽  
Assimilation System

Appropriate specification of the error statistics for both observational data and short-term forecasts is necessary to produce an optimal analysis. Observation error stems from instrument error, forward model error, and error of representation. All sources of observation error, particularly error of representation, can lead to nonzero correlations. While correlated forecast error has been accounted for since the early days of atmospheric data assimilation, observation error has typically been treated as uncorrelated until relatively recently. Thinning, averaging, and/or inflation of the assigned observation error variance have been employed to compensate for unaccounted error correlations, especially for high-resolution satellite data. In this study, the benefits of accounting for nonzero vertical (interchannel) correlation for both the Advanced Technology Microwave Satellite (ATMS) and Infrared Atmospheric Sounding Interferometer (IASI) in the NRL Atmospheric Variational Data Assimilation System-Accelerated Representer (NAVDAS-AR) are assessed. The vertical observation error covariance matrix for the ATMS and IASI instruments was estimated using the Desroziers method. The results suggest lowering the assigned error variance and introducing strong correlations, especially in the moisture-sensitive channels. Strong positive impact on forecast skill (verified against both the ECMWF analyses and high-quality radiosonde data) is shown in both the ATMS and IASI instruments. Additionally, the convergence of the iterative solver in NAVDAS-AR can be improved by small modifications to the observation error covariance matrices, resulting in further reduction in RMS error.

scholarly journals Cloud and Precipitation Parameterizations in Modeling and Variational Data Assimilation: A Review

2007 ◽  
Vol 64 (11) ◽  
pp. 3766-3784 ◽  
Author(s):  
Philippe Lopez
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Large Scale ◽  
Weather Prediction ◽  
General Circulation Models ◽  
Variational Data Assimilation ◽  
Current Status ◽  
General Question ◽  
Observation Error ◽  
Moist Processes

Abstract This paper first reviews the current status, issues, and limitations of the parameterizations of atmospheric large-scale and convective moist processes that are used in numerical weather prediction and climate general circulation models. Both large-scale (resolved) and convective (subgrid scale) moist processes are dealt with. Then, the general question of the inclusion of diabatic processes in variational data assimilation systems is addressed. The focus is put on linearity and resolution issues, the specification of model and observation error statistics, the formulation of the control vector, and the problems specific to the assimilation of observations directly affected by clouds and precipitation.

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.

The Impact of Doppler Lidar Wind Observations on a Single-Level Meteorological Analysis

2004 ◽  
Vol 43 (5) ◽  
pp. 810-820 ◽  
Author(s):  
L. P. Riishøjgaard ◽  
R. Atlas ◽  
G. D. Emmitt
Keyword(s):  
Data Assimilation ◽  
Weighted Average ◽  
Horizontal Wind ◽  
Atmospheric Flow ◽  
Single Level ◽  
Error Covariance ◽  
Analysis System ◽  
Background Fields ◽  
The Impact ◽  
Meteorological Analysis

Abstract Through the use of observation operators, modern data assimilation systems have the capability to ingest observations of quantities that are not themselves model variables but are mathematically related to those variables. An example of this is the so-called line-of-sight (LOS) winds that a spaceborne Doppler wind lidar (DWL) instrument would provide. The model or data assimilation system ideally would need information about both components of the horizontal wind vectors, whereas the observations in this case would provide only the projection of the wind vector onto a given direction. The estimated or analyzed value is then calculated essentially as a weighted average of the observation itself and the model-simulated value of the observed quantity. To assess the expected impact of a DWL, it is important to examine the extent to which a meteorological analysis can be constrained by the LOS winds. The answer to this question depends on the fundamental character of the atmospheric flow fields that are analyzed, but, just as important, it also depends on the real and assumed error covariance characteristics of these fields. A single-level wind analysis system designed to explore these issues has been built at the NASA Data Assimilation Office. In this system, simulated wind observations can be evaluated in terms of their impact on the analysis quality under various assumptions about their spatial distribution and error characteristics and about the error covariance of the background fields. The basic design of the system and experimental results obtained with it are presented. The experiments were designed to illustrate how such a system may be used in the instrument concept definition phase.

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