scholarly journals Operational Implementation of the 1D+3D-Var Assimilation Method of Radar Reflectivity Data in the AROME Model

2014 ◽  
Vol 142 (5) ◽  
pp. 1852-1873 ◽  
Author(s):  
Eric Wattrelot ◽  
Olivier Caumont ◽  
Jean-François Mahfouf
Keyword(s):  
Relative Humidity ◽  
Data Assimilation ◽  
Three Dimensional ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Short Term ◽  
Background Error ◽  
Reflectivity Data

AbstractThis paper presents results from radar reflectivity data assimilation experiments with the nonhydrostatic limited-area model Application of Research to Operations at Mesoscale (AROME) in an operational context. A one-dimensional (1D) Bayesian retrieval of relative humidity profiles followed by a three-dimensional variational data assimilation (3D-Var) technique is adopted. Several preprocessing procedures of raw reflectivity data are presented and the use of the nonrainy signal in the assimilation is widely discussed and illustrated. This two-step methodology allows the authors to build up a screening procedure that takes into account the evaluation of the results from the 1D Bayesian retrieval. In particular, the 1D retrieval is checked by comparing a pseudoanalyzed reflectivity to the observed reflectivity. Additionally, a physical consistency between the reflectivity innovations and the 1D relative humidity increments is imposed before assimilating relative humidity pseudo-observations with other observations. This allows the authors to counteract the difficulty of the current 3D-Var system to correct strong differences between model and observed clouds from the crude specification of background-error covariances. Assimilation experiments of radar reflectivity data in a preoperational configuration are first performed over a 1-month period. Positive impacts on short-term precipitation forecast scores are systematically found. The evaluation shows improvements on the analysis and also on objective conventional forecast scores, in particular for the model wind field up to 12 h. A case study for a specific precipitating system demonstrates the capacity of the method for improving significantly short-term forecasts of organized convection.

scholarly journals A GSI-Based Coupled EnSRF–En3DVar Hybrid Data Assimilation System for the Operational Rapid Refresh Model: Tests at a Reduced Resolution

2014 ◽  
Vol 142 (10) ◽  
pp. 3756-3780 ◽  
Author(s):  
Yujie Pan ◽  
Kefeng Zhu ◽  
Ming Xue ◽  
Xuguang Wang ◽  
Ming Hu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Hybrid System ◽  
Control Variable ◽  
Three Dimensional ◽  
Horizontal Resolution ◽  
Precipitation Forecast ◽  
Background Error ◽  
Hybrid Data Assimilation ◽  
Noticeable Improvement ◽  
Forecasting System

Abstract A coupled ensemble square root filter–three-dimensional ensemble-variational hybrid (EnSRF–En3DVar) data assimilation (DA) system is developed for the operational Rapid Refresh (RAP) forecasting system. The En3DVar hybrid system employs the extended control variable method, and is built on the NCEP operational gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) framework. It is coupled with an EnSRF system for RAP, which provides ensemble perturbations. Recursive filters (RF) are used to localize ensemble covariance in both horizontal and vertical within the En3DVar. The coupled En3DVar hybrid system is evaluated with 3-h cycles over a 9-day period with active convection. All conventional observations used by operational RAP are included. The En3DVar hybrid system is run at ⅓ of the operational RAP horizontal resolution or about 40-km grid spacing, and its performance is compared to parallel GSI 3DVar and EnSRF runs using the same datasets and resolution. Short-term forecasts initialized from the 3-hourly analyses are verified against sounding and surface observations. When using equally weighted static and ensemble background error covariances and 40 ensemble members, the En3DVar hybrid system outperforms the corresponding GSI 3DVar and EnSRF. When the recursive filter coefficients are tuned to achieve a similar height-dependent localization as in the EnSRF, the En3DVar results using pure ensemble covariance are close to EnSRF. Two-way coupling between EnSRF and En3DVar did not produce noticeable improvement over one-way coupling. Downscaled precipitation forecast skill on the 13-km RAP grid from the En3DVar hybrid is better than those from GSI 3DVar analyses.

scholarly journals A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data

2019 ◽  
Vol 12 (9) ◽  
pp. 4031-4051 ◽  
Author(s):  
Shizhang Wang ◽  
Zhiquan Liu
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Wrf Model ◽  
Radar Data ◽  
Poor Quality ◽  
Variational Data Assimilation ◽  
Radar Reflectivity ◽  
Background Error ◽  
Background Fields ◽  
Ice Phase

Abstract. A reflectivity forward operator and its associated tangent linear and adjoint operators (together named RadarVar) were developed for variational data assimilation (DA). RadarVar can analyze both rainwater and ice-phase species (snow and graupel) by directly assimilating radar reflectivity observations. The results of three-dimensional variational (3D-Var) DA experiments with a 3 km grid mesh setting of the Weather Research and Forecasting (WRF) model showed that RadarVar was effective at producing an analysis of reflectivity pattern and intensity similar to the observed data. Two to three outer loops with 50–100 iterations in each loop were needed to obtain a converged 3-D analysis of reflectivity, rainwater, snow, and graupel, including the melting layers with mixed-phase hydrometeors. It is shown that the deficiencies in the analysis using this operator, caused by the poor quality of the background fields and the use of the static background error covariance, can be partially resolved by using radar-retrieved hydrometeors in a preprocessing step and tuning the spatial correlation length scales of the background errors. The direct radar reflectivity assimilation using RadarVar also improved the short-term (2–5 h) precipitation forecasts compared to those of the experiment without DA.

scholarly journals Comparison of 3DVar and EnSRF Data Assimilation Using Radar Observations for the Analysis and Prediction of an MCS

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Shibo Gao ◽  
Jinzhong Min
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Mesoscale Convective System ◽  
Final Analysis ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Precipitation Forecast ◽  
Convective System ◽  
Wind Fields ◽  
Radar Observations

Using radar observations, the performances of the ensemble square root filter (EnSRF) and an indirect three-dimensional variational (3DVar) data assimilation method were compared for a mesoscale convective system (MCS) that occurred in the Front Range of the Rocky Mountains, Colorado (USA). The results showed that the root mean square innovations (RMSIs) of EnSRF were lower than 3DVar for radar reflectivity and radial velocity and that the spread of EnSRF was generally consistent with its RMSIs. EnSRF substantially improved the analysis of the MCS compared with an experiment without radar data assimilation, and it produced a slight but noticeable improvement over 3DVar in terms of both coverage and intensity. Forecast results initiated from the final analysis revealed that EnSRF generally produced the best prediction of the MCS, with improved quantitative reflectivity and precipitation forecast skills. EnSRF also demonstrated better performance than 3DVar in the prediction of neighborhood probability for reflectivity at thresholds of 20 and 35 dBZ, which better matched the observed radar reflectivity in terms of both shape and extension. Additionally, the humidity, temperature, and wind fields were also improved by EnSRF; the largest error reduction was found in the water vapor field near the surface and at upper levels.

scholarly journals A Comparison of Multiscale GSI-Based EnKF and 3DVar Data Assimilation Using Radar and Conventional Observations for Midlatitude Convective-Scale Precipitation Forecasts

2015 ◽  
Vol 143 (8) ◽  
pp. 3087-3108 ◽  
Author(s):  
Aaron Johnson ◽  
Xuguang Wang ◽  
Jacob R. Carley ◽  
Louis J. Wicker ◽  
Christopher Karstens
Keyword(s):  
Data Assimilation ◽  
Multiscale Analysis ◽  
Multiple Scales ◽  
Three Dimensional ◽  
Forecast Period ◽  
Background Error ◽  
Cold Pools ◽  
Error Covariance ◽  
Convective Scale

Abstract A GSI-based data assimilation (DA) system, including three-dimensional variational assimilation (3DVar) and ensemble Kalman filter (EnKF), is extended to the multiscale assimilation of both meso- and synoptic-scale observation networks and convective-scale radar reflectivity and velocity observations. EnKF and 3DVar are systematically compared in this multiscale context to better understand the impacts of differences between the DA techniques on the analyses at multiple scales and the subsequent convective-scale precipitation forecasts. Averaged over 10 diverse cases, 8-h precipitation forecasts initialized using GSI-based EnKF are more skillful than those using GSI-based 3DVar, both with and without storm-scale radar DA. The advantage from radar DA persists for ~5 h using EnKF, but only ~1 h using 3DVar. A case study of an upscale growing MCS is also examined. The better EnKF-initialized forecast is attributed to more accurate analyses of both the mesoscale environment and the storm-scale features. The mesoscale location and structure of a warm front is more accurately analyzed using EnKF than 3DVar. Furthermore, storms in the EnKF multiscale analysis are maintained during the subsequent forecast period. However, storms in the 3DVar multiscale analysis are not maintained and generate excessive cold pools. Therefore, while the EnKF forecast with radar DA remains better than the forecast without radar DA throughout the forecast period, the 3DVar forecast quality is degraded by radar DA after the first hour. Diagnostics revealed that the inferior analysis at mesoscales and storm scales for the 3DVar is primarily attributed to the lack of flow dependence and cross-variable correlation, respectively, in the 3DVar static background error covariance.

An Approach of Radar Reflectivity Data Assimilation and Its Assessment with the Inland QPF of Typhoon Rusa (2002) at Landfall

2007 ◽  
Vol 46 (1) ◽  
pp. 14-22 ◽  
Author(s):  
Qingnong Xiao ◽  
Ying-Hwa Kuo ◽  
Juanzhen Sun ◽  
Wen-Chau Lee ◽  
Dale M. Barker ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Mesoscale Model ◽  
Control Variable ◽  
Three Dimensional ◽  
Radar Data ◽  
Radar Reflectivity ◽  
Mixing Ratio ◽  
Warm Rain Process ◽  
Reflectivity Data

Abstract A radar reflectivity data assimilation scheme was developed within the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) three-dimensional variational data assimilation (3DVAR) system. The model total water mixing ratio was used as a control variable. A warm-rain process, its linear, and its adjoint were incorporated into the system to partition the moisture and hydrometeor increments. The observation operator for radar reflectivity was developed and incorporated into the 3DVAR. With a single reflectivity observation, the multivariate structures of the analysis increments that included cloud water and rainwater mixing ratio increments were examined. Using the onshore Doppler radar data from Jindo, South Korea, the capability of the radar reflectivity assimilation for the landfalling Typhoon Rusa (2002) was assessed. Verifications of inland quantitative precipitation forecasting (QPF) of Typhoon Rusa (2002) showed positive impacts of assimilating radar reflectivity data on the short-range QPF.

scholarly journals The impacts of uncertainties in emissions on aerosol data assimilation and short-term PM<sub>2.5</sub> predictions in CMAQ v5.2.1 over East Asia

2020 ◽  
Author(s):  
Sojin Lee ◽  
Chul Han Song ◽  
Kyung Man Han ◽  
Daven K. Henze ◽  
Kyunghwa Lee ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
East Asia ◽  
Conventional Method ◽  
Meteorological Data ◽  
Three Dimensional ◽  
Short Term ◽  
Background Error ◽  
Multi Scale ◽  
Error Covariance

Abstract. For the purpose of improving PM prediction skills in East Asia, we estimated a new background error covariance matrix (BEC) for aerosol data assimilation using surface PM2.5 observations that accounts for the uncertainties in anthropogenic emissions. In contrast to the conventional method to estimate the BEC that uses perturbations in meteorological data, this method additionally considered the perturbations using two different emission inventories. The impacts of the new BEC were then tested for the prediction of surface PM2.5 over East Asia using Community Multi-scale Air Quality (CMAQ) initialized by three-dimensional variational method (3D-VAR). The surface PM2.5 data measured at 154 sites in South Korea and 1,535 sites in China were assimilated every six hours during the Korea-United States Air Quality Study (KORUS-AQ) campaign period (1 May–14 June 2016). Data assimilation with our new BEC showed better agreement with the surface PM2.5 observations than that with the conventional method. Our method also showed closer agreement with the observations in 24-hour PM2.5 predictions with ~ 44 % fewer negative biases than the conventional method. We conclude that increased standard deviations, together with horizontal and vertical length scales in the new BEC, tend to improve the data assimilation and short-term predictions for the surface PM2.5. This paper also suggests further research efforts devoted to estimating the BEC to improve PM2.5 predictions.

scholarly journals A Study of the HWRF Analysis and Forecast Impact of Realistically Simulated CYGNSS Observations Assimilated as Scalar Wind Speeds and as VAM Wind Vectors

2018 ◽  
Vol 146 (7) ◽  
pp. 2221-2236 ◽  
Author(s):  
Bachir Annane ◽  
Brian McNoldy ◽  
S. Mark Leidner ◽  
Ross Hoffman ◽  
Robert Atlas ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Satellite System ◽  
Analysis Method ◽  
Short Term ◽  
Background Error ◽  
Wind Speeds ◽  
Ocean Surface Winds ◽  
Global Navigation Satellite ◽  
Data Assimilation System

Abstract In preparation for the launch of the NASA Cyclone Global Navigation Satellite System (CYGNSS), a variety of observing system simulation experiments (OSSEs) were conducted to develop, tune, and assess methods of assimilating these novel observations of ocean surface winds. From a highly detailed and realistic hurricane nature run (NR), CYGNSS winds were simulated with error characteristics that are expected to occur in reality. The OSSE system makes use of NOAA’s HWRF Model and GSI data assimilation system in a configuration that was operational in 2012. CYGNSS winds were assimilated as scalar wind speeds and as wind vectors determined by a variational analysis method (VAM). Both forms of wind information had positive impacts on the short-term HWRF forecasts, as shown by key storm and domain metrics. Data assimilation cycle intervals of 1, 3, and 6 h were tested, and the 3-h impacts were consistently best. One-day forecasts from CYGNSS VAM vector winds were the most dynamically consistent with the NR. The OSSEs have a number of limitations; the most noteworthy is that this is a case study, and static background error covariances were used.

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

2013 ◽  
Vol 6 (12) ◽  
pp. 3563-3576 ◽  
Author(s):  
S. Federico
Keyword(s):  
Relative Humidity ◽  
Data Assimilation ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Wind Profiler ◽  
Short Term ◽  
Background Error ◽  
Data Assimilation System ◽  
Rams Model ◽  
Assimilation System

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.

Direct Assimilation of Radar Reflectivity Data Using 3DVAR: Treatment of Hydrometeor Background Errors and OSSE Tests

2019 ◽  
Vol 147 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Chengsi Liu ◽  
Ming Xue ◽  
Rong Kong
Keyword(s):  
Three Dimensional ◽  
Error Variance ◽  
Radar Reflectivity ◽  
Temperature Dependent ◽  
Background Error ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Homogeneous Background ◽  
Reflectivity Data ◽  
Error Profiles

Despite the well-known importance of background error covariance in data assimilation, not much study has been focused on its impact on the assimilation of radar reflectivity within a three-dimensional variational (3DVar) framework. In this study, it is shown that unphysical analysis increments of hydrometeors are produced when using vertically homogeneous background error variance. This issue cannot be fully solved by using the so-called hydrometeor classification in the reflectivity observation operator. Alternatively, temperature-dependent background error profiles for hydrometeor control variables are proposed. With such a treatment, the vertical background error profiles are specified to be temperature dependent, allowing for more physical partitioning of radar-observed precipitation information among the liquid and ice hydrometeors. The 3DVar analyses using our treatment are compared with those using constant background error or “hydrometeor classification” through observing system simulation experiments with a simulated supercell storm. Results show that 1) 3DVar with constant hydrometeor background errors produces unphysical rainwater at the high levels and unphysical snow at the low levels; 2) the hydrometeor classification approach reduces unphysical rainwater and snow at those levels, but the analysis increments are still unphysically spread in the vertical by the background error covariance when the vertically invariant background errors are used; and 3) the temperature-dependent background error profiles enable physically more reasonable analyses of liquid and ice hydrometeors from reflectivity assimilation.

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