Evaluation of Combined Satellite and Radar Data Assimilation with POD-4DEnVar Method on Rainfall Forecast

Satellite and radar observations represent two fundamentally different remote sensing observation types, providing independent information for numerical weather prediction (NWP). Because the individual impact on improving forecast has previously been examined, combining these two resources of data potentially enhances the performance of weather forecast. In this study, satellite radiance, radar radial velocity and reflectivity are simultaneously assimilated with the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar). The impact is evaluated on continuous severe rainfall processes occurred from June to July in 2016 and 2017. Results show that combined assimilation of satellite and radar data with POD-4DEnVar has the potential to improve weather forecast. Averaged over 22 forecasts, RMSEs indicate that though the forecast results are sensitive to different variables, generally the improvement is found in different pressure levels with assimilation. The precipitation skill scores are generally increased when assimilation is carried out. A case study is also examined to figure out the contributions to forecast improvement. Better intensity and distribution of precipitation forecast is found in the accumulated rainfall evolution with POD-4DEnVar assimilation. These improvements are attributed to the local changes in moisture, temperature and wind field. In addition, with radar data assimilation, the initial rainwater and cloud water conditions are changed directly. Both experiments can simulate the strong hydrometeor in the precipitation area, but assimilation spins up faster, strengthening the initial intensity of the heavy rainfall. Generally, the combined assimilation of satellite and radar data results in better rainfall forecast than without data assimilation.

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An Ice-Phase Microphysics Forward Model and Preliminary Results of Polarimetric Radar Data Assimilation

Monthly Weather Review ◽

10.1175/mwr-d-16-0035.1 ◽

2017 ◽

Vol 145 (2) ◽

pp. 683-708 ◽

Cited By ~ 10

Author(s):

Xuanli Li ◽

John R. Mecikalski ◽

Derek Posselt

Keyword(s):

Data Assimilation ◽

Radial Velocity ◽

Weather Prediction ◽

Radar Data ◽

Forward Model ◽

Precipitation Forecast ◽

Upper Troposphere ◽

The Impact ◽

Radar Data Assimilation ◽

Ice Phase

In this study, an ice-phase microphysics forward model has been developed for the Weather Research and Forecasting (WRF) Model three-dimensional variational data assimilation (WRF 3D-Var) system. Radar forward operators for reflectivity and the polarimetric variable, specific differential phase ( KDP), have been built into the ice-phase WRF 3D-Var package to allow modifications in liquid (cloud water and rain) and solid water (cloud ice and snow) fields through data assimilation. Experiments have been conducted to assimilate reflectivity and radial velocity observations collected by the Weather Surveillance Radar-1988 Doppler (WSR-88D) in Hytop, Alabama, for a mesoscale convective system (MCS) on 15 March 2008. Numerical results have been examined to assess the impact of the WSR-88D data using the ice-phase WRF 3D-Var radar data assimilation package. The main goals are to first demonstrate radar data assimilation with an ice-phase microphysics forward model and second to improve understanding on how to enhance the utilization of radar data in numerical weather prediction. Results showed that the assimilation of reflectivity and radial velocity data using the ice-phase system provided significant improvement especially in the mid- to upper troposphere. The improved initial conditions led to apparent improvement in the short-term precipitation forecast of the MCS. An additional experiment has been conducted to explore the assimilation of KDP data collected by the Advanced Radar for Meteorological and Operational Research (ARMOR). Results showed that KDP data have been successfully assimilated using the ice-phase 3D-Var package. A positive impact of the KDP data has been found on rainwater in the lower troposphere and snow in the mid- to upper troposphere.

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Processing of 3D Weather Radar Data with Application for Assimilation in the NWP Model

Miscellanea Geographica ◽

10.2478/mgrsd-2014-0023 ◽

2014 ◽

Vol 18 (3) ◽

pp. 31-39 ◽

Cited By ~ 1

Author(s):

Katarzyna Ośródka ◽

Jan Szturc ◽

Bogumił Jakubiak ◽

Anna Jurczyk

Keyword(s):

Quality Control ◽

Data Assimilation ◽

Prediction Models ◽

Initial Conditions ◽

Weather Prediction ◽

Weather Radar ◽

Radar Data ◽

Field Pattern ◽

The Impact ◽

Radar Data Assimilation

Abstract The paper is focused on the processing of 3D weather radar data to minimize the impact of a number of errors from different sources, both meteorological and non-meteorological. The data is also quantitatively characterized in terms of its quality. A set of dedicated algorithms based on analysis of the reflectivity field pattern is described. All the developed algorithms were tested on data from the Polish radar network POLRAD. Quality control plays a key role in avoiding the introduction of incorrect information into applications using radar data. One of the quality control methods is radar data assimilation in numerical weather prediction models to estimate initial conditions of the atmosphere. The study shows an experiment with quality controlled radar data assimilation in the COAMPS model using the ensemble Kalman filter technique. The analysis proved the potential of radar data for such applications; however, further investigations will be indispensable.

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Using Radar Wind Observations to Improve Mesoscale Numerical Weather Prediction

Weather and Forecasting ◽

10.1175/waf936.1 ◽

2006 ◽

Vol 21 (4) ◽

pp. 502-522 ◽

Cited By ~ 27

Author(s):

Qingyun Zhao ◽

John Cook ◽

Qin Xu ◽

Paul R. Harasti

Keyword(s):

High Resolution ◽

Data Assimilation ◽

Numerical Weather Prediction ◽

Weather Prediction ◽

Three Dimensional ◽

Radar Data ◽

The Impact ◽

Data Assimilation System ◽

Radar Data Assimilation ◽

Assimilation System

Abstract A high-resolution radar data assimilation system is presented for high-resolution numerical weather prediction models. The system is under development at the Naval Research Laboratory for the Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System. A variational approach is used to retrieve three-dimensional dynamical fields of atmospheric conditions from multiple-Doppler radar observations of radial velocity within a limited area. The methodology is described along with a preliminary evaluation of the impact of assimilated radar data on model forecasts using a case study of a squall line that occurred along the east coast of the United States on 9 May 2003. Results from the experiments show a significant impact from the assimilated radar radial velocity data on the model forecast of not just dynamical but also hydrological fields at all model levels for the duration of the storm. A verification system has also been developed to assess the radar data assimilation impact, and the results show improvements in the three-dimensional wind forecasts but relatively small changes in the prediction of storm locations. This study highlights the need to develop a continuous radar data assimilation system to maximize the impact of the data.

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Analysis and Prediction of a Squall Line Observed during IHOP Using Multiple WSR-88D Observations

Monthly Weather Review ◽

10.1175/2007mwr2205.1 ◽

2008 ◽

Vol 136 (7) ◽

pp. 2364-2388 ◽

Cited By ~ 42

Author(s):

Juanzhen Sun ◽

Ying Zhang

Keyword(s):

Data Assimilation ◽

Radar Data ◽

Squall Line ◽

Short Term ◽

Accurate Analysis ◽

The Individual ◽

Convective Cells ◽

The Impact ◽

Radar Data Assimilation

Abstract This paper presents a case study on the assimilation of observations from multiple Doppler radars of the Next Generation Weather Radar (NEXRAD) network. A squall-line case documented during the International H2O Project (IHOP_2002) is used for the study. Radar radial velocity and reflectivity observations from four NEXRADs are assimilated into a convection-permitting model using a four-dimensional variational data assimilation (4DVAR) scheme. A mesoscale analysis using a supplementary sounding, velocity–azimuth display (VAD) profiles, and surface observations from Meteorological Aerodrome Reports (METAR) are produced and used to provide a background and boundary conditions for the 4DVAR radar data assimilation. Impact of the radar data assimilation is assessed by verifying the skill of the subsequent very short-term (5 h) forecasts. Assimilation and forecasting experiments are conducted to examine the impact of radar data assimilation on the subsequent precipitation forecasts. It is found that the 4DVAR radar data assimilation significantly reduces the model spinup required in the experiments without radar data assimilation, resulting in significantly improved 5-h forecasts. Additional experiments are conducted to study the sensitivity of the precipitation forecasts with respect to 4DVAR cycling configurations. Results from these experiments suggest that the forecasts with three 4DVAR cycles are improved over those with cold start, but the cycling impact seems to diminish with more cycles. The impact of observations from each of the individual radars is also examined by conducting a set of experiments in which data from each radar are alternately excluded. It is found that the accurate analysis of the environmental wind surrounding the convective cells is important in successfully predicting the squall line.

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Applications of Radar Data Assimilation with Hydrometeor Control Variables within the WRFDA on the Prediction of Landfalling Hurricane IKE (2008)

Atmosphere ◽

10.3390/atmos12070853 ◽

2021 ◽

Vol 12 (7) ◽

pp. 853

Author(s):

Feifei Shen ◽

Jinzhong Min ◽

Hong Li ◽

Dongmei Xu ◽

Aiqing Shu ◽

...

Keyword(s):

Data Assimilation ◽

Inner Core ◽

Radar Data ◽

Precipitation Forecast ◽

Small Scale ◽

Hurricane Ike ◽

Control Variables ◽

Model Background ◽

The Impact ◽

Radar Data Assimilation

The impact of assimilating radar radial velocity and reflectivity on the analyses and forecast of Hurricane IKE is investigated within the framework of the WRF (Weather Research and Forecasting) model and its three-dimensional variational (3DVar) data assimilation system, including the hydrometeor control variables. Hurricane IKE in the year 2008 was chosen as the study case. It was found that assimilating radar data is able to effectively improve the small-scale information of the hurricane vortex area in the model background. Radar data assimilation experiments yield significant cyclonic wind increments in the inner-core area of the hurricane, enhancing the intensity of the hurricane in the model background. On the other hand, by extending the traditional control variables to include the hydrometeor control variables, the assimilation of radar reflectivity can effectively adjust the water vapor and hydrometeors of the background, further improving the track and intensity forecast of the hurricane. The precipitation forecast skill is also enhanced to some extent with the radar data assimilation, especially with the extended hydrometeor control variables.

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Improved Streamflow Forecast in a Small-Medium Sized River Basin with Coupled WRF and WRF-Hydro: Effects of Radar Data Assimilation

Remote Sensing ◽

10.3390/rs13163251 ◽

2021 ◽

Vol 13 (16) ◽

pp. 3251

Author(s):

Tianwei Gu ◽

Yaodeng Chen ◽

Yufang Gao ◽

Luyao Qin ◽

Yuqing Wu ◽

...

Keyword(s):

Data Assimilation ◽

Weather Prediction ◽

Coupling Model ◽

Radar Data ◽

Flood Forecasting ◽

Disaster Mitigation ◽

Precipitation Forecast ◽

Distributed Hydrological Model ◽

Mountainous Regions ◽

Radar Data Assimilation

Accurate and long leading time flood forecasting is very important for flood disaster mitigation. It is an effective method to couple the Quantitative Precipitation Forecast (QPF) products provided by Numerical Weather Prediction (NWP) models to a distributed hydrological model with the goal of extending the leading time for flood forecasting. However, the QPF products contain a certain degree of uncertainty and would affect the accuracy of flood forecasting, especially in the mountainous regions. Radar data assimilation plays an important role in improving the quality of QPF and further improves flood forecasting. In this paper, radar data assimilation was applied in order to construct a high-resolution atmospheric-hydrological coupling model based on the WRF and WRF-Hydro models. Four experiments with conventional observational and radar data assimilation were conducted to evaluate the flood forecasting capability of this coupled model in a small-medium sized basin based on eight typical flood events. The results show that the flood forecast skills are highly QPF-dependent. The QPF from the WRF model is improved by assimilating radar data and further increasing the accuracy of flood forecasting, although both precipitation and flood are slightly over-forecasted. However, the improvements by assimilating conventional observational data are not obvious. In general, radar data assimilation can improve flood forecasting effectively in a small-medium sized basin based on the atmospheric-hydrological coupling model.

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Predictability of Deep Convection in Idealized and Operational Forecasts: Effects of Radar Data Assimilation, Orography, and Synoptic Weather Regime

Monthly Weather Review ◽

10.1175/mwr-d-19-0045.1 ◽

2019 ◽

Vol 148 (1) ◽

pp. 63-81 ◽

Cited By ~ 10

Author(s):

Kevin Bachmann ◽

Christian Keil ◽

George C. Craig ◽

Martin Weissmann ◽

Christian A. Welzbacher

Keyword(s):

Data Assimilation ◽

Deep Convection ◽

Radar Data ◽

Ensemble Prediction ◽

Small Scale ◽

Model Errors ◽

Ensemble Prediction System ◽

Beneficial Impact ◽

The Impact ◽

Radar Data Assimilation

Abstract We investigate the practical predictability limits of deep convection in a state-of-the-art, high-resolution, limited-area ensemble prediction system. A combination of sophisticated predictability measures, namely, believable and decorrelation scale, are applied to determine the predictable scales of short-term forecasts in a hierarchy of model configurations. First, we consider an idealized perfect model setup that includes both small-scale and synoptic-scale perturbations. We find increased predictability in the presence of orography and a strongly beneficial impact of radar data assimilation, which extends the forecast horizon by up to 6 h. Second, we examine realistic COSMO-KENDA simulations, including assimilation of radar and conventional data and a representation of model errors, for a convectively active two-week summer period over Germany. The results confirm increased predictability in orographic regions. We find that both latent heat nudging and ensemble Kalman filter assimilation of radar data lead to increased forecast skill, but the impact is smaller than in the idealized experiments. This highlights the need to assimilate spatially and temporally dense data, but also indicates room for further improvement. Finally, the examination of operational COSMO-DE-EPS ensemble forecasts for three summer periods confirms the beneficial impact of orography in a statistical sense and also reveals increased predictability in weather regimes controlled by synoptic forcing, as defined by the convective adjustment time scale.

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Impact of the assimilation of lightning data on the precipitation forecast at different forecast ranges

Advances in Science and Research ◽

10.5194/asr-14-187-2017 ◽

2017 ◽

Vol 14 ◽

pp. 187-194 ◽

Cited By ~ 5

Author(s):

Stefano Federico ◽

Marco Petracca ◽

Giulia Panegrossi ◽

Claudio Transerici ◽

Stefano Dietrich

Keyword(s):

Data Assimilation ◽

Mesoscale Model ◽

Weather Prediction ◽

Horizontal Resolution ◽

Probability Of Detection ◽

Precipitation Forecast ◽

Time Range ◽

False Alarms ◽

Forecast Time ◽

The Impact

Abstract. This study investigates the impact of the assimilation of total lightning data on the precipitation forecast of a numerical weather prediction (NWP) model. The impact of the lightning data assimilation, which uses water vapour substitution, is investigated at different forecast time ranges, namely 3, 6, 12, and 24 h, to determine how long and to what extent the assimilation affects the precipitation forecast of long lasting rainfall events (> 24 h). The methodology developed in a previous study is slightly modified here, and is applied to twenty case studies occurred over Italy by a mesoscale model run at convection-permitting horizontal resolution (4 km). The performance is quantified by dichotomous statistical scores computed using a dense raingauge network over Italy. Results show the important impact of the lightning assimilation on the precipitation forecast, especially for the 3 and 6 h forecast. The probability of detection (POD), for example, increases by 10 % for the 3 h forecast using the assimilation of lightning data compared to the simulation without lightning assimilation for all precipitation thresholds considered. The Equitable Threat Score (ETS) is also improved by the lightning assimilation, especially for thresholds below 40 mm day−1. Results show that the forecast time range is very important because the performance decreases steadily and substantially with the forecast time. The POD, for example, is improved by 1–2 % for the 24 h forecast using lightning data assimilation compared to 10 % of the 3 h forecast. The impact of the false alarms on the model performance is also evidenced by this study.

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Advancing from Convection-Allowing NWP to Warn-on-Forecast: Evidence of Progress

Weather and Forecasting ◽

10.1175/waf-d-17-0145.1 ◽

2018 ◽

Vol 33 (2) ◽

pp. 599-607 ◽

Cited By ~ 20

Author(s):

John R. Lawson ◽

John S. Kain ◽

Nusrat Yussouf ◽

David C. Dowell ◽

Dustan M. Wheatley ◽

...

Keyword(s):

Data Assimilation ◽

Real Time ◽

Initial Conditions ◽

Spatial Scales ◽

Weather Prediction ◽

Radar Data ◽

Model Systems ◽

Deterministic Systems ◽

Forecast Lead Time ◽

The Impact

Abstract The Warn-on-Forecast (WoF) program, driven by advanced data assimilation and ensemble design of numerical weather prediction (NWP) systems, seeks to advance 0–3-h NWP to aid National Weather Service warnings for thunderstorm-induced hazards. An early prototype of the WoF prediction system is the National Severe Storms Laboratory (NSSL) Experimental WoF System for ensembles (NEWSe), which comprises 36 ensemble members with varied initial conditions and parameterization suites. In the present study, real-time 3-h quantitative precipitation forecasts (QPFs) during spring 2016 from NEWSe members are compared against those from two real-time deterministic systems: the operational High Resolution Rapid Refresh (HRRR, version 1) and an upgraded, experimental configuration of the HRRR. All three model systems were run at 3-km horizontal grid spacing and differ in initialization, particularly in the radar data assimilation methods. It is the impact of this difference that is evaluated herein using both traditional and scale-aware verification schemes. NEWSe, evaluated deterministically for each member, shows marked improvement over the two HRRR versions for 0–3-h QPFs, especially at higher thresholds and smaller spatial scales. This improvement diminishes with forecast lead time. The experimental HRRR model, which became operational as HRRR version 2 in August 2016, also provides added skill over HRRR version 1.

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A Velocity Dealiasing Technique Using Rapidly Updated Analysis from a Four-Dimensional Variational Doppler Radar Data Assimilation System

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2010jtecha1300.1 ◽

2010 ◽

Vol 27 (7) ◽

pp. 1140-1152 ◽

Cited By ~ 16

Author(s):

Eunha Lim ◽

Juanzhen Sun

Keyword(s):

Data Assimilation ◽

Doppler Radar ◽

Weather Prediction ◽

Radar Data ◽

Objective Analysis ◽

Doppler Velocity ◽

Horizontal Shear ◽

Data Assimilation System ◽

Radar Data Assimilation ◽

Assimilation System

Abstract A Doppler velocity dealiasing algorithm is developed within the storm-scale four-dimensional radar data assimilation system known as the Variational Doppler Radar Analysis System (VDRAS). The innovative aspect of the algorithm is that it dealiases Doppler velocity at each grid point independently by using three-dimensional wind fields obtained either from an objective analysis using conventional observations and mesoscale model output or from a rapidly updated analysis of VDRAS that assimilates radar data. This algorithm consists of three steps: preserving horizontal shear, global dealiasing using reference wind from the objective analysis or the VDRAS analysis, and local dealiasing. It is automated and intended to be used operationally for radar data assimilation using numerical weather prediction models. The algorithm was tested with 384 volumes of radar data observed from the Next Generation Weather Radar (NEXRAD) for a severe thunderstorm that occurred during 15 June 2002. It showed that the algorithm was effective in dealiasing large areas of aliased velocities when the wind from the objective analysis was used as the reference and that more accurate dealiasing was achieved by using the continuously cycled VDRAS analysis.

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