scholarly journals Improving the Model Convective Storm Quantitative Precipitation Nowcasting by Assimilating State Variables Retrieved from Multiple-Doppler Radar Observations

2014 ◽  
Vol 142 (11) ◽  
pp. 4017-4035 ◽  
Author(s):  
Yu-Chieng Liou ◽  
Jian-Luen Chiou ◽  
Wei-Hao Chen ◽  
Hsin-Yu Yu
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Scale Up ◽  
Three Dimensional ◽  
Radar Data ◽  
State Variables ◽  
Weather System ◽  
Additional Information ◽  
Convective Scale ◽  
Fine Resolution

Abstract This research combines an advanced multiple-Doppler radar synthesis technique with the thermodynamic retrieval method, originally proposed by Gal-Chen, and a moisture/temperature adjustment scheme, and formulates a sequential procedure. The focus is on applying this procedure to improve the model quantitative precipitation nowcasting (QPN) skill in the convective scale up to 3 hours. A series of (observing system simulation experiment) OSSE-type tests and a real case study are conducted to investigate the performance of this algorithm under different conditions. It is shown that by using the retrieved three-dimensional wind, thermodynamic, and microphysical parameters to reinitialize a fine-resolution numerical model, its QPN skill can be significantly improved. Since the Gal-Chen method requires the horizontal average properties of the weather system at each altitude, utilization of in situ radiosonde(s) to obtain this additional information for the retrieval is tested. When sounding data are not available, it is demonstrated that using the model results to replace the role played by observing devices is also a feasible choice. The moisture field is obtained through a simple, but effective, adjusting scheme and is found to be beneficial to the rainfall forecast within the first hour after the reinitialization of the model. Since this algorithm retrieves the unobserved state variables instantaneously from the wind measurements and directly uses them to reinitialize the model, fewer radar data and a shorter model spinup time are needed to correct the rainfall forecasts, in comparison with other data assimilation techniques such as four-dimensional variational data assimilation (4DVAR) or ensemble Kalman filter (EnKF) methods.

scholarly journals The Impact of Covariance Localization for Radar Data on EnKF Analyses of a Developing MCS: Observing System Simulation Experiments

2013 ◽  
Vol 141 (11) ◽  
pp. 3691-3709 ◽  
Author(s):  
Ryan A. Sobash ◽  
David J. Stensrud
Keyword(s):  
System Simulation ◽  
Three Dimensional ◽  
Radar Data ◽  
Convective System ◽  
State Variables ◽  
Simulation Experiments ◽  
Length Scales ◽  
Convective Scale ◽  
The Impact ◽  
Prior State

Abstract Several observing system simulation experiments (OSSEs) were performed to assess the impact of covariance localization of radar data on ensemble Kalman filter (EnKF) analyses of a developing convective system. Simulated Weather Surveillance Radar-1988 Doppler (WSR-88D) observations were extracted from a truth simulation and assimilated into experiments with localization cutoff choices of 6, 12, and 18 km in the horizontal and 3, 6, and 12 km in the vertical. Overall, increasing the horizontal localization and decreasing the vertical localization produced analyses with the smallest RMSE for most of the state variables. The convective mode of the analyzed system had an impact on the localization results. During cell mergers, larger horizontal localization improved the results. Prior state correlations between the observations and state variables were used to construct reverse cumulative density functions (RCDFs) to identify the correlation length scales for various observation-state pairs. The OSSE with the smallest RMSE employed localization cutoff values that were similar to the horizontal and vertical length scales of the prior state correlations, especially for observation-state correlations above 0.6. Vertical correlations were restricted to state points closer to the observations than in the horizontal, as determined by the RCDFs. Further, the microphysical state variables were correlated with the reflectivity observations on smaller scales than the three-dimensional wind field and radial velocity observations. The ramifications of these findings on localization choices in convective-scale EnKF experiments that assimilate radar data are discussed.

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 Impact of the Dual-Polarization Doppler Radar Data on Two Convective Storms with a Warm-Rain Radar Forward Operator

2012 ◽  
Vol 140 (7) ◽  
pp. 2147-2167 ◽  
Author(s):  
Xuanli Li ◽  
John R. Mecikalski
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Three Dimensional ◽  
Radar Data ◽  
Dual Polarization ◽  
Convective Storms ◽  
Warm Rain ◽  
Mesoscale Convective ◽  
Differential Reflectivity ◽  
The Impact

Abstract The dual-polarization (dual pol) Doppler radar can transmit/receive both horizontally and vertically polarized power returns. The dual-pol radar measurements have been shown to provide a more accurate precipitation estimate compared to traditional radars. In this study, the horizontal reflectivity ZH, differential reflectivity ZDR, specific differential phase KDP, and radial velocity VR collected by the C-band Advanced Radar for Meteorological and Operational Research (ARMOR) are assimilated for two convective storms. A warm-rain scheme is constructed to assimilate ZH, ZDR, and KDP data using the three-dimensional variational data assimilation (3DVAR) system with the Advanced Research Weather Research and Forecasting Model (ARW-WRF). The main goals of this study are first to demonstrate and compare the impact of various dual-pol variables in initialization of real case convective storms and second to test how the dual-pol fields may be better used with a 3DVAR system. The results show that the ZH, ZDR, KDP, and VR data substantially improve the initial condition for two mesoscale convective storms. Significant positive impacts on short-term forecast are obtained for both storms. Additionally, KDP and ZDR data assimilation is shown to be superior to ZH and ZDR and ZH-only data assimilation when the warm-rain microphysics is adopted. With the ongoing upgrade of the current Weather Surveillance Radar-1988 Doppler (WSR-88D) network to include dual-pol capabilities (started in early 2011), the findings from this study can be a helpful reference for utilizing the dual-pol radar data in numerical simulations of severe weather and related quantitative precipitation forecasts.

Importance of Horizontally Inhomogeneous Environmental Initial Conditions to Ensemble Storm-Scale Radar Data Assimilation and Very Short-Range Forecasts

2010 ◽  
Vol 138 (4) ◽  
pp. 1250-1272 ◽  
Author(s):  
David J. Stensrud ◽  
Jidong Gao
Keyword(s):  
Data Assimilation ◽  
Short Range ◽  
Doppler Radar ◽  
Environmental Variability ◽  
Initial Conditions ◽  
Radar Data ◽  
Ensemble Member ◽  
Radar Observations ◽  
Convective Scale ◽  
Background Fields

Abstract The assimilation of operational Doppler radar observations into convection-resolving numerical weather prediction models for very short-range forecasting represents a significant scientific and technological challenge. Numerical experiments over the past few years indicate that convective-scale forecasts are sensitive to the details of the data assimilation methodology, the quality of the radar data, the parameterized microphysics, and the storm environment. In this study, the importance of horizontal environmental variability to very short-range (0–1 h) convective-scale ensemble forecasts initialized using Doppler radar observations is investigated for the 4–5 May 2007 Greensburg, Kansas, tornadic thunderstorm event. Radar observations of reflectivity and radial velocity from the operational Doppler radar network at 0230 UTC 5 May 2007, during the time of the first large tornado, are assimilated into each ensemble member using a three-dimensional variational data assimilation system (3DVAR) developed at the Center for Analysis and Prediction of Storms (CAPS). Very short-range forecasts are made using the nonhydrostatic Advanced Regional Prediction System (ARPS) model from each ensemble member and the results are compared with the observations. Explicit three-dimensional environmental variability information is provided to the convective-scale ensemble using analyses from a 30-km mesoscale ensemble data assimilation system. Comparisons between convective-scale ensembles with initial conditions produced by 3DVAR using 1) background fields that are horizontally homogeneous but vertically inhomogeneous (i.e., have different vertical environmental profiles) and 2) background fields that are horizontally and vertically inhomogeneous are undertaken. Results show that the ensemble with horizontally and vertically inhomogeneous background fields provides improved predictions of thunderstorm structure, mesocyclone track, and low-level circulation track than the ensemble with horizontally homogeneous background fields. This suggests that knowledge of horizontal environmental variability is important to successful convective-scale ensemble predictions and needs to be included in real-data experiments.

scholarly journals A Scheme to Assimilate “No Rain” Observations from Doppler Radar

2018 ◽  
Vol 33 (1) ◽  
pp. 71-88 ◽  
Author(s):  
Shibo Gao ◽  
Juanzhen Sun ◽  
Jinzhong Min ◽  
Ying Zhang ◽  
Zhuming Ying
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Prediction Models ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Grid Point ◽  
Three Dimensional ◽  
Radar Data ◽  
Wind Fields ◽  
Radar Data Assimilation

Abstract Radar reflectivity observations contain valuable information on precipitation and have been assimilated into numerical weather prediction models for improved microphysics initialization. However, low-reflectivity (or so-called no rain) echoes have often been ignored or not effectively used in radar data assimilation schemes. In this paper, a scheme to assimilate no-rain radar observations is described within the framework of the Weather Research and Forecasting Model’s three-dimensional variational data assimilation (3DVar) system, and its impact on precipitation forecasts is demonstrated. The key feature of the scheme is a neighborhood-based approach to adjusting water vapor when a grid point is deemed as no rain. The performance of the scheme is first examined using a severe convective case in the Front Range of the Colorado Rocky Mountains and then verified by running the 3DVar system in the same region, with and without the no-rain assimilation scheme for 68 days and 3-hourly rapid update cycles. It is shown that the no-rain data assimilation method reduces the bias and false alarm ratio of precipitation over its counterpart without that assimilation. The no-rain assimilation also improved humidity, temperature, and wind fields, with the largest error reduction in the water vapor field, both near the surface and at upper levels. It is also shown that the advantage of the scheme is in its ability to conserve total water content in cycled radar data assimilation, which cannot be achieved by assimilating only precipitation echoes.

scholarly journals Design Strategies of an Hourly Update 3DVAR Data Assimilation System for Improved Convective Forecasting

2016 ◽  
Vol 31 (5) ◽  
pp. 1673-1695 ◽  
Author(s):  
Wenxue Tong ◽  
Gang Li ◽  
Juanzhen Sun ◽  
Xiaowen Tang ◽  
Ying Zhang
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Dynamic Balance ◽  
Three Dimensional ◽  
Precipitation Forecast ◽  
Design Strategies ◽  
Weather System ◽  
New Techniques ◽  
Quantitative Precipitation Forecast ◽  
Convective Scale

Abstract This study examines two strategies for improving the analysis of an hourly update three-dimensional variational data assimilation (3DVAR) system and the subsequent quantitative precipitation forecast (QPF). The first strategy is to assimilate synoptic and radar observations in different steps. This strategy aims to extract both large-scale and convective-scale information from observations typically representing different scales. The second strategy is to add a divergence constraint to the momentum variables in the 3DVAR system. This technique aims at improving the dynamic balance and suppressing noise introduced during the assimilation process. A detailed analysis on how the new techniques impact convective-scale QPF was conducted using a severe storm case over Colorado and Kansas during 8 and 9 August 2008. First, it is demonstrated that, without the new strategies, the QPF initialized with an hourly update analysis performs worse than its 3-hourly counterpart. The implementation of the two-step assimilation and divergence constraint in the hourly update system results in improved QPF throughout most of the 12-h forecast period. The diagnoses of the analysis fields show that the two-step assimilation is able to preserve key convective-scale as well as large-scale structures that are consistent with the development of the real weather system. The divergence constraint is effective in improving the balance between the momentum control variables in the analysis, which leads to less spurious convection and improved QPF scores. The improvements of the new techniques were further verified by eight convective cases in 2014 and shown to be statistically significant.

scholarly journals Retrieval of three-dimensional wind fields from Doppler radar data using an efficient two-step approach

2010 ◽  
Vol 3 (5) ◽  
pp. 4459-4495 ◽  
Author(s):  
C. López Carrillo ◽  
D. J. Raymond
Keyword(s):  
Variational Approach ◽  
Continuity Equation ◽  
Doppler Radar ◽  
Three Dimensional ◽  
Relevant Information ◽  
Radar Data ◽  
Wind Fields ◽  
Wind Retrieval ◽  
Complex Sampling ◽  
Doppler Radar Data

Abstract. In this work, we describe an efficient approach for wind retrieval from dual Doppler radar data. The approach produces a gridded field that not only satisfies the observations, but also satisfies the anelastic mass continuity equation. The method is based on the so-called three-dimensional variational approach to the retrieval of wind fields from radar data. The novelty consists in separating the task into steps that reduce the amount of data processed by the global minimization algorithm, while keeping the most relevant information from the radar observations. The method is flexible enough to incorporate observations from several radars, accommodate complex sampling geometries, and readily include dropsonde or sounding observations in the analysis. We demonstrate the usefulness of our method by analyzing a real case with data collected during the TPARC/TCS-08 field campaign.

scholarly journals Climatological Features of the Weakly and Very Stably Stratified Nocturnal Boundary Layers. Part I: State Variables Containing Information about Regime Occupation

2019 ◽  
Vol 76 (11) ◽  
pp. 3455-3484 ◽  
Author(s):  
Carsten Abraham ◽  
Adam H. Monahan
Keyword(s):  
Wind Speed ◽  
Turbulence Intensity ◽  
Three Dimensional ◽  
Static Stability ◽  
Wind Component ◽  
State Variables ◽  
Intermittent Turbulence ◽  
Additional Information ◽  
State Variable ◽  
Mean Wind Speed

Abstract The atmospheric nocturnal stable boundary layer (SBL) can be classified into two distinct regimes: the weakly SBL (wSBL) with sustained turbulence and the very SBL (vSBL) with weak and intermittent turbulence. A hidden Markov model (HMM) analysis of the three-dimensional state-variable space of Reynolds-averaged mean dry static stability, mean wind speed, and wind speed shear is used to classify the SBL into these two regimes at nine different tower sites, in order to study long-term regime occupation and transition statistics. Both Reynolds-averaged mean data and measures of turbulence intensity (eddy variances) are separated in a physically meaningful way. In particular, fluctuations of the vertical wind component are found to be much smaller in the vSBL than in the wSBL. HMM analyses of these data using more than two SBL regimes do not result in robust results across measurement locations. To identify which meteorological state variables carry the information about regime occupation, the HMM analyses are repeated using different state-variable subsets. Reynolds-averaged measures of turbulence intensity (such as turbulence kinetic energy) at any observed altitude hold almost the same information as the original set, without adding any additional information. In contrast, both stratification and shear depend on surface information to capture regime transitions accurately. Use of information only in the bottom 10 m of the atmosphere is sufficient for HMM analyses to capture important information about regime occupation and transition statistics. It follows that the commonly measured 10-m wind speed is potentially a good indicator of regime occupation.

scholarly journals Development of a Hybrid En3DVar Data Assimilation System and Comparisons with 3DVar and EnKF for Radar Data Assimilation with Observing System Simulation Experiments

2018 ◽  
Vol 146 (1) ◽  
pp. 175-198 ◽  
Author(s):  
Rong Kong ◽  
Ming Xue ◽  
Chengsi Liu
Keyword(s):  
Data Assimilation ◽  
System Simulation ◽  
Radar Data ◽  
Mixing Ratio ◽  
State Variables ◽  
Ensemble Size ◽  
Simulation Experiments ◽  
Background Error ◽  
Variational Minimization ◽  
Water Vapor Mixing Ratio

Abstract A hybrid ensemble–3DVar (En3DVar) system is developed and compared with 3DVar, EnKF, “deterministic forecast” EnKF (DfEnKF), and pure En3DVar for assimilating radar data through perfect-model observing system simulation experiments (OSSEs). DfEnKF uses a deterministic forecast as the background and is therefore parallel to pure En3DVar. Different results are found between DfEnKF and pure En3DVar: 1) the serial versus global nature and 2) the variational minimization versus direct filter updating nature of the two algorithms are identified as the main causes for the differences. For 3DVar (EnKF/DfEnKF and En3DVar), optimal decorrelation scales (localization radii) for static (ensemble) background error covariances are obtained and used in hybrid En3DVar. The sensitivity of hybrid En3DVar to covariance weights and ensemble size is examined. On average, when ensemble size is 20 or larger, a 5%–10% static covariance gives the best results, while for smaller ensembles, more static covariance is beneficial. Using an ensemble size of 40, EnKF and DfEnKF perform similarly, and both are better than pure and hybrid En3DVar overall. Using 5% static error covariance, hybrid En3DVar outperforms pure En3DVar for most state variables but underperforms for hydrometeor variables, and the improvement (degradation) is most notable for water vapor mixing ratio qυ (snow mixing ratio qs). Overall, EnKF/DfEnKF performs the best, 3DVar performs the worst, and static covariance only helps slightly via hybrid En3DVar.

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