scholarly journals Impact of radar data assimilation using WRF three-dimensional variational system, for the simulation of a heavy rainfall case in Central Italy

2013 ◽  
Vol 6 (4) ◽  
pp. 7315-7353
Author(s):  
I. Maiello ◽  
R. Ferretti ◽  
S. Gentile ◽  
M. Montopoli ◽  
E. Picciotti ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Heavy Rainfall ◽  
Initial Conditions ◽  
Positive Impact ◽  
Central Italy ◽  
Three Dimensional ◽  
Warm Start ◽  
The Impact ◽  
Dwr Data

Abstract. This work is a first assessment of the role of Doppler Weather radar (DWR) data in a mesoscale model for the prediction of a heavy rainfall. The study analyzes the event occurred during 19–22 May 2008 in the urban area of Rome. The impact of the radar reflectivity and radial velocity acquired from Monte Midia Doppler radar, on the assimilation into the Weather Research Forecasting (WRF) model version 3.2, is discussed. The goal is to improve the WRF high resolution initial condition by assimilating DWR data and using ECMWF analyses as First Guess thus improving the forecast of surface rainfall. Several experiments are performed using different set of Initial Conditions (ECMWF analyses and warm start or cycling) and a different assimilation strategy (3 h-data assimilation cycle). In addition, 3DVAR (three-dimensional variational) sensitivity tests to outer loops are performed for each of the previous experiment to include the non-linearity in the observation operators. In order to identify the best ICs, statistical indicators such as forecast accuracy, frequency bias, false alarm rate and equitable threat score for the accumulated precipitation are used. The results show that the assimilation of DWR data has a positive impact on the prediction of the heavy rainfall of this event, both assimilating reflectivity and radial velocity, together with conventional observations. Finally, warm start results in more accurate experiments as well as the outer loops strategy.

scholarly journals Impact of radar data assimilation for the simulation of a heavy rainfall case in central Italy using WRF–3DVAR

2014 ◽  
Vol 7 (9) ◽  
pp. 2919-2935 ◽  
Author(s):  
I. Maiello ◽  
R. Ferretti ◽  
S. Gentile ◽  
M. Montopoli ◽  
E. Picciotti ◽  
...  
Keyword(s):  
Heavy Rainfall ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Positive Impact ◽  
Central Italy ◽  
Wrf Model ◽  
Forecast Accuracy ◽  
Radar Data ◽  
Heavy Rainfall Event ◽  
Dwr Data

Abstract. The aim of this study is to investigate the role of the assimilation of Doppler weather radar (DWR) data in a mesoscale model for the forecast of a heavy rainfall event that occurred in Italy in the urban area of Rome from 19 to 22 May 2008. For this purpose, radar reflectivity and radial velocity acquired from Monte Midia Doppler radar are assimilated into the Weather Research Forecasting (WRF) model, version 3.4.1. The general goal is to improve the quantitative precipitation forecasts (QPF): with this aim, several experiments are performed using the three-dimensional variational (3DVAR) technique. Moreover, sensitivity tests to outer loops are performed to include non-linearity in the observation operators. In order to identify the best initial conditions (ICs), statistical indicators such as forecast accuracy, frequency bias, false alarm rate and equitable threat score for the accumulated precipitation are used. The results show that the assimilation of DWR data has a large impact on both the position of convective cells and on the rainfall forecast of the analyzed event. A positive impact is also found if they are ingested together with conventional observations. Sensitivity to the use of two or three outer loops is also found if DWR data are assimilated together with conventional data.

scholarly journals OSSEs for an Ensemble 3DVAR Data Assimilation System with Radar Observations of Convective Storms

2016 ◽  
Vol 73 (6) ◽  
pp. 2403-2426 ◽  
Author(s):  
Jidong Gao ◽  
Chenghao Fu ◽  
David J. Stensrud ◽  
John S. Kain
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Initial Conditions ◽  
Positive Impact ◽  
Three Dimensional ◽  
Field Analysis ◽  
Cold Pool ◽  
Ensemble Forecasts ◽  
Convective Scale

Abstract An ensemble of the three-dimensional variational data assimilation (En3DA) method for convective-scale weather has been developed. It consists of an ensemble of three-dimensional variational data assimilations and forecasts in which member differences are introduced by perturbing initial conditions and/or observations, and it uses flow-dependent error covariances generated by the ensemble forecasts. The method is applied to the assimilation of simulated radar data for a supercell storm. Results indicate that the flow-dependent ensemble covariances are effective in enabling convective-scale analyses, as the most important features of the simulated storm, including the low-level cold pool and midlevel mesocyclone, are well analyzed. Several groups of sensitivity experiments are conducted to test the robustness of the method. The first group demonstrates that incorporating a mass continuity equation as a weak constraint into the En3DA algorithm can improve the quality of the analyses when radial velocity observations contain large errors. In the second group of experiments, the sensitivity of analyses to the microphysical parameterization scheme is explored. Results indicate that the En3DA analyses are quite sensitive to differences in the microphysics scheme, suggesting that ensemble forecasts with multiple microphysics schemes could reduce uncertainty related to model physics errors. Experiments also show that assimilating reflectivity observations can reduce spinup time and that it has a small positive impact on the quality of the wind field analysis. Of the threshold values tested for assimilating reflectivity observations, 15 dBZ provides the best analysis. The final group of experiments demonstrates that it is not necessary to perturb radial velocity observations for every ensemble number in order to improve the quality of the analysis.

scholarly journals Impacts of Assimilating ATMS Radiances on Heavy Rainfall Forecast in RMAPS-ST

2020 ◽  
Vol 12 (7) ◽  
pp. 1147
Author(s):  
Yanhui Xie ◽  
Min Chen ◽  
Jiancheng Shi ◽  
Shuiyong Fan ◽  
Jing He ◽  
...  
Keyword(s):  
Heavy Rainfall ◽  
Initial Conditions ◽  
Positive Impact ◽  
Weather Prediction ◽  
Advanced Technology ◽  
Added Value ◽  
Rainfall Forecast ◽  
Hourly Rainfall ◽  
Radiance Data ◽  
The Impact

The Advanced Technology Microwave Sounder (ATMS) mounted on the Suomi National Polar-Orbiting Partnership (NPP) satellite can provide both temperature and humidity information for a weather prediction model. Based on the rapid-refresh multi-scale analysis and prediction system—short-term (RMAPS-ST), we investigated the impact of ATMS radiance data assimilation on strong rainfall forecasts. Two groups of experiments were conducted to forecast heavy precipitation over North China between 18 July and 20 July 2016. The initial conditions and forecast results from the two groups of experiments have been compared and evaluated against observations. In comparison with the first group of experiments that only assimilated conventional observations, some added value can be obtained for the initial conditions of temperature, humidity, and wind fields after assimilating ATMS radiance observations in the system. For the forecast results with the assimilation of ATMS radiances, the score skills of quantitative forecast rainfall have been improved when verified against the observed rainfall. The Heidke skill score (HSS) skills of 6-h accumulated precipitation in the 24-h forecasts were overall increased, more prominently so for the heavy rainfall above 25 mm in the 0–6 h of forecasts. Assimilating ATMS radiance data reduced the false alarm ratio of quantitative precipitation forecasting in the 0–12 h of the forecast range and thus improved the threat scores for the heavy rainfall storm. Furthermore, the assimilation of ATMS radiances improved the spatial distribution of hourly rainfall forecast with observations compared with that of the first group of experiments, and the mean absolute error was reduced in the 10-h lead time of forecasts. The inclusion of ATMS radiances provided more information for the vertical structure of features in the temperature and moisture profiles, which had an indirect positive impact on the forecasts of the heavy rainfall in the RMAPS-ST system. However, the deviation in the location of the heavy rainfall center requires future work.

scholarly journals Development of a hybrid variational-ensemble data assimilation technique for observed lightning tested in a mesoscale model

2014 ◽  
Vol 21 (5) ◽  
pp. 1027-1041 ◽  
Author(s):  
K. Apodaca ◽  
M. Zupanski ◽  
M. DeMaria ◽  
J. A. Knaff ◽  
L. D. Grasso
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Positive Impact ◽  
Weather Prediction ◽  
New Information ◽  
Forecasting System ◽  
Maximum Likelihood Ensemble Filter ◽  
The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Geostationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), and improving initial conditions during several data assimilation cycles. However, the 6 h forecast after the assimilation did not show a clear improvement in terms of root mean square (RMS) errors.

Improving air quality forecasting with the assimilation of GOCI AOD retrievals

2020 ◽  
Author(s):  
Soyoung Ha ◽  
Zhiquan Liu
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
South Korea ◽  
Positive Impact ◽  
Fine Particulate Matter ◽  
Three Dimensional ◽  
Basis Set ◽  
Upstream Region ◽  
The Impact ◽  
Air Quality Forecasting

<p>The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal and spatial resolution every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean peninsula. In this study, the GOCI Aerosol optical depth (AOD), retrieved at 550 nm wavelength, is assimilated to ameliorate the analysis quality, thereby making systematic improvements on air quality forecasting in South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3DVAR) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Over the Korea-United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of air quality forecasting is examined by comparing with other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and fine particulate matter (PM2.5) observations at the surface. Consistent with previous studies, the assimilation of surface PM2.5 concentrations alone systematically underestimates surface PM2.5 and its positive impact lasts mainly for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecasts are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea. The talk will be finished with an introduction of our ongoing efforts on developing the assimilation capability for more sophisticated aerosol schemes such as Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) and the Modal Aerosol Dynamics Model for Europe (MADE)-Volatility basis set (VBS).</p>

scholarly journals CIRA/CSU Four-Dimensional Variational Data Assimilation System

2005 ◽  
Vol 133 (4) ◽  
pp. 829-843 ◽  
Author(s):  
Milija Zupanski ◽  
Dusanka Zupanski ◽  
Tomislava Vukicevic ◽  
Kenneth Eis ◽  
Thomas Vonder Haar
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Control Variable ◽  
Three Dimensional ◽  
Atmospheric Modeling ◽  
Variational Data Assimilation ◽  
Regional Atmospheric Modeling ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System

A new four-dimensional variational data assimilation (4DVAR) system is developed at the Cooperative Institute for Research in the Atmosphere (CIRA)/Colorado State University (CSU). The system is also called the Regional Atmospheric Modeling Data Assimilation System (RAMDAS). In its present form, the 4DVAR system is employing the CSU/Regional Atmospheric Modeling System (RAMS) nonhydrostatic primitive equation model. The Weather Research and Forecasting (WRF) observation operator is used to access the observations, adopted from the WRF three-dimensional variational data assimilation (3DVAR) algorithm. In addition to the initial conditions adjustment, the RAMDAS includes the adjustment of model error (bias) and lateral boundary conditions through an augmented control variable definition. Also, the control variable is defined in terms of the velocity potential and streamfunction instead of the horizontal winds. The RAMDAS is developed after the National Centers for Environmental Prediction (NCEP) Eta 4DVAR system, however with added improvements addressing its use in a research environment. Preliminary results with RAMDAS are presented, focusing on the minimization performance and the impact of vertical correlations in error covariance modeling. A three-dimensional formulation of the background error correlation is introduced and evaluated. The Hessian preconditioning is revisited, and an alternate algebraic formulation is presented. The results indicate a robust minimization performance.

scholarly journals The Impact of Airborne Doppler Aerosol Wind (DAWN) Lidar Wind Profiles on Numerical Simulations of Tropical Convective Systems during the NASA Convective Processes Experiment (CPEX)

2020 ◽  
Vol 37 (4) ◽  
pp. 705-722 ◽  
Author(s):  
Zhiqiang Cui ◽  
Zhaoxia Pu ◽  
G. David Emmitt ◽  
Steven Greco
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Three Dimensional ◽  
Tropical Storm ◽  
Convective System ◽  
Spatiotemporal Resolution ◽  
Convective Systems ◽  
Convective Processes ◽  
Wind Profiles ◽  
The Impact

AbstractHigh-spatiotemporal-resolution airborne Doppler Aerosol Wind (DAWN) lidar profiles over the Caribbean Sea and Gulf of Mexico region were collected during the NASA Convective Processes Experiment (CPEX) field campaign from 27 May to 24 June 2017. This study examines the impact of assimilating these wind profiles on the numerical simulation of moist convective systems using an Advanced Research version of the Weather Research and Forecasting (WRF) Model (WRF-ARW). A mesoscale convective system and a tropical storm (Cindy) that occurred on 16 June 2017 in a strong shear environment and on 21 June 2017 in a weak shear environment, respectively, are selected as case studies. The DAWN wind profiles are assimilated with the NCEP Gridpoint Statistical Interpolation analysis system using a three-dimensional variational (3DVar) and a hybrid three-dimensional ensemble-variational (3DEnVar) data assimilation systems to provide the initial conditions for a short-range forecast. Results show that the assimilation of DAWN wind profiles has significant positive impacts on convective simulations with the two assimilation approaches. The assimilation of DAWN wind profiles creates notable adjustments in the analysis of the divergence field for WRF simulations with a good agreement of wind forecasts with radiosonde observations. The quantitative precipitation forecasting is also improved. In general, the 3DEnVar data assimilation method is deemed more promising for DAWN data assimilation. There are cases with Tropical Storm Cindy in which DAWN data have slight to neutral impact on rainfall forecasts with 3DVAR, implying complicated interactions between errors of retrieved wind data and background error covariance in the lower and upper troposphere.

scholarly journals Hybrid variational-ensemble assimilation of lightning observations in a mesoscale model

2014 ◽  
Vol 1 (1) ◽  
pp. 917-952
Author(s):  
K. Apodaca ◽  
M. Zupanski ◽  
M. DeMaria ◽  
J. A. Knaff ◽  
L. D. Grasso
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Mesoscale Model ◽  
Initial Conditions ◽  
Positive Impact ◽  
Weather Prediction ◽  
New Information ◽  
Forecasting System ◽  
Maximum Likelihood Ensemble Filter ◽  
The Impact

Abstract. Lightning measurements from the Geostationary Lightning Mapper (GLM) that will be aboard the Goestationary Operational Environmental Satellite – R Series will bring new information that can have the potential for improving the initialization of numerical weather prediction models by assisting in the detection of clouds and convection through data assimilation. In this study we focus on investigating the utility of lightning observations in mesoscale and regional applications suitable for current operational environments, in which convection cannot be explicitly resolved. Therefore, we examine the impact of lightning observations on storm environment. Preliminary steps in developing a lightning data assimilation capability suitable for mesoscale modeling are presented in this paper. World Wide Lightning Location Network (WWLLN) data was utilized as a proxy for GLM measurements and was assimilated with the Maximum Likelihood Ensemble Filter, interfaced with the Nonhydrostatic Mesoscale Model core of the Weather Research and Forecasting system (WRF-NMM). In order to test this methodology, regional data assimilation experiments were conducted. Results indicate that lightning data assimilation had a positive impact on the following: information content, influencing several dynamical variables in the model (e.g., moisture, temperature, and winds), improving initial conditions, and partially improving WRF-NMM forecasts during several data assimilation cycles.

scholarly journals Impact of Atmospheric and Aerosol Optical Depth Observations on Aerosol Initial Conditions in a strongly-coupled data assimilation system

2019 ◽  
Author(s):  
Milija Zupanski ◽  
Anton Kliewer ◽  
Ting-Chi Wu ◽  
Karina Apodaca ◽  
Qijing Bian ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Initial Conditions ◽  
Positive Impact ◽  
Storm Event ◽  
Strongly Coupled ◽  
Atmospheric Observations ◽  
Coupled Data Assimilation ◽  
The Impact

Abstract. Strongly coupled data assimilation frameworks provide a mechanism for including additional information about aerosols through the coupling between aerosol and atmospheric variables, effectively utilizing atmospheric observations to change the aerosol analysis. Here, we investigate the impact of these observations on aerosol using the Maximum Likelihood Ensemble Filter (MLEF) algorithm with Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) which includes the Godard Chemistry Aerosol Radiation and Transport (GOCART) module. We apply this methodology to a dust storm event over the Arabian Peninsula and examine in detail the error covariance and in particular the impact of atmospheric observations on improving the aerosol initial conditions. The assimilated observations include conventional atmospheric observations and Aerosol Optical Depth (AOD) retrievals. Results indicate a positive impact of using strongly coupled data assimilation and atmospheric observations on the aerosol initial conditions, quantified using Degrees of Freedom for Signal.

scholarly journals Impact of 3DVAR Data Assimilation on the Prediction of Heavy Rainfall over Southern China

2013 ◽  
Vol 2013 ◽  
pp. 1-17 ◽  
Author(s):  
Tuanjie Hou ◽  
Fanyou Kong ◽  
Xunlai Chen ◽  
Hengchi Lei
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Positive Impact ◽  
Southern China ◽  
Precipitation Forecast ◽  
Radiosonde Data ◽  
Near Surface ◽  
Rainfall Events ◽  
Forecasting System ◽  
The Impact

This study examines the impact of three-dimensional variational data assimilation (3DVAR) on the prediction of two heavy rainfall events over Southern China by using a real-time storm-scale forecasting system. Initialized from the European Centre for Medium-Range Weather Forecasts (ECMWF) high-resolution data, the forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting (WRF-ARW) model and the Advanced Regional Prediction System (ARPS) 3DVAR package. Observations from Doppler radars, surface Automatic Weather Station (AWS) network, and radiosondes are used in the experiments to evaluate the impact of data assimilation on short-term quantitative precipitation forecast (QPF) skill. Results suggest that extrasurface AWS data assimilation has slight but general positive impact on rainfall location forecasts. Surface AWS data also improve model results of near-surface variables. Radiosonde data assimilation improves the QPF skill by improving rainfall position accuracy and reducing rainfall overprediction. Compared with radar data, the overall impact of additional surface and radiosonde data is smaller and is reflected primarily in reducing rainfall overestimation. The assimilation of all radar, surface, and radiosonde data has a more positive impact on the forecast skill than the assimilation of either type of data only for the two rainfall events.

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