Assimilation of Surface-Based Boundary Layer Profiler Observations during a Cool-Season Weather Event Using an Observing System Simulation Experiment. Part I: Analysis Impact

Otkin, Jason A.; Hartung, Daniel C.; Turner, David D.; Petersen, Ralph A.; Feltz, Wayne F.; Janzon, Erik

doi:10.1175/2011mwr3622.1

Cited by 35 publications

(28 citation statements)

References 41 publications

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“…Hence, while the basic aim of the paper is to show the general characteristics of the 3D-Var, an application to the data assimilation of tropospheric profiles is also given. Indeed, the numerical experiment set-up of this paper should be of interest to the atmospheric profiling community because it can be used in OSE (observing system experiment), which allows for the objective assessment and comparison of existing observing systems, or in OSSE (observing system simulation experiment), whose aim is to show the impact of next generation observing systems in a controlled software environment such as weather prediction models (Otkin et al, 2011;Moninger et al, 2010).…”

Section: S Federico: Implementation Of a 3d-var Systemmentioning

confidence: 99%

Implementation of a 3D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

Federico

2013

Atmos. Meas. Tech.

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Abstract. This paper presents the current status of development of a three-dimensional variational data assimilation system (3D-Var). The system can be used with different numerical weather prediction models, but it is mainly designed to be coupled with the Regional Atmospheric Modelling System (RAMS). Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height.Important features of the data assimilation system are the use of incremental formulation of the cost function, and the representation of the background error by recursive filters and the eigenmodes of the vertical component of the background error covariance matrix. This matrix is estimated by the National Meteorological Center (NMC) method.The data assimilation and forecasting system is applied to the real context of atmospheric profiling data assimilation, and in particular to the short-term wind prediction. The analyses are produced at 20 km horizontal resolution over central Europe and extend over the whole troposphere. Assimilated data are vertical soundings of wind, temperature, and relative humidity from radiosondes, and wind measurements of the European wind profiler network.Results show the validity of the analyses because they are closer to the observations (lower root mean square error (RMSE)) compared to the background (higher RMSE), and the differences of the RMSEs are in agreement with the data assimilation settings.To quantify the impact of improved initial conditions on the short-term forecast, the analyses are used as initial conditions of three-hours forecasts of the RAMS model. In particular two sets of forecasts are produced: (a) the first uses the ECMWF analysis/forecast cycle as initial and boundary conditions; (b) the second uses the analyses produced by the 3D-Var as initial conditions, then it is driven by the ECMWF forecast.The improvement is quantified by considering the horizontal components of the wind, which are measured at asynoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range. The results are in agreement with the set-up of the numerical experiment.

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Section: S Federico: Implementation Of a 3d-var Systemmentioning

confidence: 99%

Implementation of a 3D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

Federico

2013

Atmos. Meas. Tech.

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“…More recently, an Observing System Simulation Experiment (OSSE) considering a simulated network of 140 MWRs was carried out for a winter storm case (Otkin et al , ; Hartung et al , ). The Weather Research and Forecasting (WRF) model (Skamarock et al , ) and an ensemble Kalman filter (EnKF) algorithm were used to assimilate simulated MWR temperature and humidity profiles at a horizontal resolution of 18 km every hour during a period of 24 h. Overall, the authors found that the assimilation of MWR data had a positive impact on temperature and humidity analyses.…”

Section: Introductionmentioning

confidence: 99%

Assimilation of humidity and temperature observations retrieved from ground‐based microwave radiometers into a convective‐scale NWP model

Caumont

Cimini²,

Löhnert

et al. 2016

Quart J Royal Meteoro Soc

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Temperature and humidity retrievals from an international network of ground-based microwave radiometers (MWRs) have been collected to assess the potential of their assimilation into a convective-scale numerical weather prediction (NWP) system. Thirteen stations over a domain encompassing the western Mediterranean basin were considered for a time period of 41 days in autumn, when heavy precipitation events most often plague this area.Prior to their assimilation, MWR data were compared to very-short-term forecasts. Observation-minus-background statistics revealed some biases, but standard deviations were comparable to that obtained with radiosondes. The MWR data were then assimilated in a three-dimensional variational data assimilation system through the use of a rapid update cycle. A first set of four different experiments were designed to assess the impact of the assimilation of temperature and humidity profiles, both separately and jointly. This assessment was done through the use of a comprehensive dataset of upper-air and surface observations collected in the framework of the HyMeX programme.The results showed that the impact was generally very limited on all verified parameters, except for precipitation. The impact was found to be generally beneficial in terms of most verification metrics for about 18 h, especially for larger accumulations. Two additional data-denial experiments showed that even more positive impact could be obtained when MWR data were assimilated without other redundant observations. The conclusion of the study points to possible ways of enhancing the impact of the assimilation of MWR data in convective-scale NWP systems.

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“…OSSEs have also been conducted using a 3-month NASA generated Nature Run to evaluate the impact of wind lidar data on hurricane prediction (Atlas and Emmitt, 2008). By using an OSSE with an ensemble Kalman filter data assimilation system, Otkin et al (2011) demonstrated the potential of an array of ground-based remote sensing boundary-layer profiling instruments to improve the accuracy of wintertime atmospheric analyses over land. Yussouf and Stensrud (2010) applied a similar approach to study the impact of phased array radar observations on very short-range prediction of severe thunderstorms.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Earth Systems Research Laboratory’s global Observing System Simulation Experiment system

Privé

Xie

Woollen

et al. 2013

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T An Observing System Simulation Experiment (OSSE) system has been implemented at the National Oceanographic and Atmospheric Administration Earth Systems Research Laboratory in the US as part of an international Joint OSSE effort. The setup of the OSSE consists of a Nature Run from a 13-month free run of the European Center for Medium-Range Weather Forecasts operational model, synthetic observations developed at the National Centers for Environmental Prediction (NCEP) and the National Aeronautics and Space Administration Global Modelling and Assimilation Office, and an operational version of the NCEP Gridpoint Statistical Interpolation data assimilation and Global Forecast System numerical weather prediction model. Synthetic observations included both conventional observations and the following radiance observations: AIRS, AMSU-A, AMSU-B, HIRS2, HIRS3, MSU, GOES radiance and OSBUV. Calibration was performed by modifying the error added to the conventional synthetic observations to achieve a match between data denial impacts on the analysis state in the OSSE system and in the real data system. Following calibration, the performance of the OSSE system was evaluated in terms of forecast skill scores and impact of observations on forecast fields.

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Assimilation of Surface-Based Boundary Layer Profiler Observations during a Cool-Season Weather Event Using an Observing System Simulation Experiment. Part I: Analysis Impact

Cited by 35 publications

References 41 publications

Implementation of a 3D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

Implementation of a 3D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

Assimilation of humidity and temperature observations retrieved from ground‐based microwave radiometers into a convective‐scale NWP model

Evaluation of the Earth Systems Research Laboratory’s global Observing System Simulation Experiment system

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