Collaborative Adaptive Sensing Of The Atmosphere Research Articles

Exploring Ensemble Forecast Sensitivity to Observations for a Convective-Scale Data Assimilation System over the Dallas–Fort Worth Testbed

Abstract Forecast sensitivity to observation (FSO) methods have become increasingly popular over the past two decades, providing the ability to quantify the impacts of various observing systems on forecasts without having to conduct costly data denial experiments. While adjoint- and ensemble-based FSO are employed in many global operational systems, their use for regional convection-allowing data assimilation (DA) and forecast systems have not been fully examined. In this study, ensemble FSO (EFSO) is explored for high-frequency convective-scale DA for a severe weather case study over the Dallas–Fort Worth testbed. This testbed, originally established by the Collaborative Adaptive Sensing of the Atmosphere (CASA) project, aims to improve high-resolution DA systems by assimilating a variety of existing state and regional mesoscale observing systems to fill gaps of conventional observing networks. This study utilizes EFSO to estimate relative impacts of nonconventional surface observations against conventional observations, and further incorporates assimilated radar observations into EFSO. Results show that, when applying advected localization and a neighborhood upscale averaging technique, EFSO estimates remain correlated and skillful with the actual error reduction of all assimilated observations for the duration of 2-h forecasts. The ability for EFSO to verify against other metrics (surface T, u, υ, q) beside energy norms is also demonstrated, emphasizing that EFSO can be used to evaluate impacts of specific parts of the forecast system rather than integrated quantities. Partitioned EFSO revealed that while conventional and radar observations contributed to most of the total energy, nonconventional observations contributed a significant percentage (up to 25%) of the total impact to surface thermodynamic fields.

Assessing the Impact of Non-Conventional Radar and Surface Observations on High-Resolution Analyses and Forecasts of a Severe Hailstorm

A 2009 National Research Council study recommended that new mesoscale observing networks be integrated with existing networks to form a nationwide “network of networks”. The report also recommended that research testbeds be established, such as the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) DFW Testbed, to ascertain the potential benefit of proposed observing systems. In this work, we use various conventional and non-conventional observing systems from the DFW Testbed in a series of observing system experiments (OSEs). Of special interest are radar data from Terminal Doppler Weather Radars and CASA X-band radars, as well as novel surface observations. The Advanced Regional Prediction System (ARPS) model is used to perform OSEs that are designed to assess the impact of these observing systems. A three-dimensional variational analysis system and companion complex cloud analysis are used to produce analysis increments, which are assimilated in ARPS using Incremental Analysis Updating. Experiments are performed on a supercell thunderstorm case from 11 April 2016 that produced large, damaging hail. The analysis includes quantitative comparisons of model-derived hail with radar-observed hail, along with verification of surface fields. The CASA radial velocity data benefited the forecasted storm structure, as it positively affected subsequent storm morphology and model-derived hail forecasts. Of note in surface observation impacts, the dewpoint measurements from the non-conventional Earth Networks and CWOP networks slightly degrade the forecasted dewpoint field compared to independent standard observations, but did not prevent the successful prediction of hail.

CASA Prediction System over Dallas–Fort Worth Urban Network: Blending of Nowcasting and High-Resolution Numerical Weather Prediction Model

AbstractThis study targeted improving Collaborative Adaptive Sensing of the Atmosphere’s (CASA) 6-h lead time predictive ability by blending the radar-based nowcast with the NWP model over the Dallas–Fort Worth (DFW) urban radar network. This study also depicts the recent updates in CASA’s real-time reflectivity nowcast system by assessing nine precipitation cases over the DFW urban region. CASA’s nowcast framework displayed better primer outcomes than the WRF Model forecast for the lead time of 1 h and 30 min. After that time, the predictive ability of the nowcast framework began decreasing compared to the WRF Model. To broaden CASA’s predictive system lead time to 6 h, the WRF Model forecasts were blended with Dynamic and Adaptive Radar Tracking of Storms (DARTS) nowcast. The HRRR model analysis was used as initial and boundary conditions in the WRF Model. The high-resolution dual-pol radar observations were assimilated into the WRF Model through the 3DVAR data assimilation technique. Three kinds of blending strategies were used and the results were compared: 1) hyperbolic tangent curve (HTW), 2) critical success index (CSIW), and 3) salient cross dissolve (Sal CD). The sensitivity studies were conducted to decide desirable parameters in the blending techniques. The outcomes proved that blending enhanced the prediction skills. Also, the overall performance of blending relies on the accuracy of the WRF forecast. Even though blending results are mixed, the HTW-based technique performed better than the other two techniques.

Stochastic Spectral Method for Radar-Based Probabilistic Precipitation Nowcasting

AbstractNowcasts (short-term forecasts) of heavy rainfall causing flash floods are highly valuable in densely populated urban areas. In the Collaborative Adaptive Sensing of the Atmosphere (CASA) project, a high-resolution X-band radar network was deployed in the Dallas–Fort Worth (DFW) metroplex. The Dynamic and Adaptive Radar Tracking of Storms (DARTS) method was developed as a part of the CASA nowcasting system. In this method, the advection field is determined in the spectral domain using the discrete Fourier transform. DARTS was recently extended to include a filtering scheme for suppressing small-scale precipitation features that have low predictability. Building on the earlier work, Stochastic DARTS (S-DARTS), a probabilistic extension of DARTS, is developed and tested using the CASA DFW radar network. In this method, the nowcasts are stochastically perturbed in order to simulate uncertainties. Two novel features are introduced in S-DARTS. First, the scale filtering and perturbation based on an autoregressive model are done in the spectral domain in order to achieve high computational efficiency. Second, this methodology is extended to modeling the temporal evolution of the advection field. The performance and forecast skill of S-DARTS are evaluated with different precipitation intensity thresholds and ensemble sizes. It is shown that S-DARTS can produce reliable probabilistic nowcasts in the CASA DFW domain with 250-m spatial resolution up to 45 min for lower precipitation intensities (below 2 mm h−1). For higher intensities (above 5 mm h−1), adequate skill can be obtained up to 15 min.

Fully Spectral Method for Radar-Based Precipitation Nowcasting

Short-term forecasts (nowcasts) of severe rainfall and flooding are of high importance to the society. In the collaborative adaptive sensing of the atmosphere (CASA) project, a high-resolution X -band radar network was deployed in the Dallas–Fort Worth (DFW) urban area. The dynamic and adaptive radar tracking of storms (DARTS) is a key component of the precipitation nowcasting system that was developed in the CASA project. In DARTS, the advection field determination is formulated in the spectral domain using the discrete Fourier transform (DFT). Building on the earlier work, an extension of DARTS is proposed. The novelty of the proposed scale filtering (SF-DARTS) method is the formulation of the extrapolation also in the spectral domain. The extrapolation method is combined with autoregressive AR(2) models applied to Fourier frequency bands together with adaptive truncation of DFT coefficients. This effectively filters small spatial scales having low predictability. It is shown that the proposed approach improves forecast skill and gives improved computational efficiency compared to conventional methods. Another important contribution is that DARTS is being evaluated for the first time beyond the urban scale. DARTS and SF-DARTS are evaluated using data from two different sources, namely the urban-scale CASA DFW network (200 km), and the country-wide radar network operated by the Finnish Meteorological Institute (1000 km).

Toward high-resolution flash flood prediction in large urban areas – Analysis of sensitivity to spatiotemporal resolution of rainfall input and hydrologic modeling

• Sensitivity of streamflow simulation to hydrologic modeling resolution is assessed. • Two radar-based precipitation input of different resolution are used for comparison. • Streamflow prediction improved with higher resolution up to about 500 m and 15 min. • There are trade-offs among resolution and timeliness and accuracy of prediction. Urban flash flooding is a serious problem in large, highly populated areas such as the Dallas–Fort Worth Metroplex (DFW). Being able to monitor and predict flash flooding at a high spatiotemporal resolution is critical to providing location-specific early warnings and cost-effective emergency management in such areas. Under the idealized conditions of perfect models and precipitation input, one may expect that spatiotemporal specificity and accuracy of the model output improve as the resolution of the models and precipitation input increases. In reality, however, due to the errors in the precipitation input, and in the structures, parameters and states of the models, there are practical limits to the model resolution. In this work, we assess the sensitivity of streamflow simulation in urban catchments to the spatiotemporal resolution of precipitation input and hydrologic modeling to identify the resolution at which the simulation errors may be at minimum given the quality of the precipitation input and hydrologic models used, and the response time of the catchment. The hydrologic modeling system used in this work is the National Weather Service (NWS) Hydrology Laboratory’s Research Distributed Hydrologic Model (HLRDHM) applied at spatiotemporal resolutions ranging from 250 m to 2 km and from 1 min to 1 h applied over the Cities of Fort Worth, Arlington and Grand Prairie in DFW. The high-resolution precipitation input is from the DFW Demonstration Network of the Collaborative Adaptive Sensing of the Atmosphere (CASA) radars. For comparison, the NWS Multisensor Precipitation Estimator (MPE) product, which is available at a 4-km 1-h resolution, was also used. The streamflow simulation results are evaluated for 5 urban catchments ranging in size from 3.4 to 54.6 km 2 and from about 45 min to 3 h in time-to-peak in the Cities of Fort Worth, Arlington and Grand Prairie. The streamflow observations used in evaluation were obtained from water level measurements via rating curves derived from 1-D steady-state non-uniform hydraulic modeling. The results indicate that a spatiotemporal resolution of 500 m and 15 min or higher is a good choice for streamflow prediction using HLRDHM and CASA QPE in the study area, but that, due to the nonlinear accretion of random errors in QPE and imperfect model dynamics, there are trade-offs to consider among resolution, timeliness of prediction and prediction accuracy.

The quantitative precipitation estimation system for Dallas–Fort Worth (DFW) urban remote sensing network

• Dual-polarization radar can provide better QPE than traditional single-pol radar. • National weather radar network was upgraded with dual-polarization capability. • Dense networks of small radars can fill the sampling gap of today’s large installations. • High-resolution QPE can be achieved by combining large national radar with small gap-filling radars. The Dallas–Fort Worth (DFW) urban radar network consists of a combination of high resolution X band radars and a standard National Weather Service (NWS) Next-Generation Radar (NEXRAD) system operating at S band frequency. High spatiotemporal-resolution quantitative precipitation estimation (QPE) is one of the important applications of such a network. This paper presents a real-time QPE system developed by the Collaborative Adaptive Sensing of the Atmosphere (CASA) Engineering Research Center for the DFW urban region using both the high resolution X band radar network and the NWS S band radar observations. The specific dual-polarization radar rainfall algorithms at different frequencies (i.e., S- and X-band) and the fusion methodology combining observations at different temporal resolution are described. Radar and rain gauge observations from four rainfall events in 2013 that are characterized by different meteorological phenomena are used to compare the rainfall estimation products of the CASA DFW QPE system to conventional radar products from the national radar network provided by NWS. This high-resolution QPE system is used for urban flash flood mitigations when coupled with hydrological models.

Improving high-resolution quantitative precipitation estimation via fusion of multiple radar-based precipitation products

Summary For monitoring and prediction of water-related hazards in urban areas such as flash flooding, high-resolution hydrologic and hydraulic modeling is necessary. Because of large sensitivity and scale dependence of rainfall–runoff models to errors in quantitative precipitation estimates (QPE), it is very important that the accuracy of QPE be improved in high-resolution hydrologic modeling to the greatest extent possible. With the availability of multiple radar-based precipitation products in many areas, one may now consider fusing them to produce more accurate high-resolution QPE for a wide spectrum of applications. In this work, we formulate and comparatively evaluate four relatively simple procedures for such fusion based on Fisher estimation and its conditional bias-penalized variant: Direct Estimation (DE), Bias Correction (BC), Reduced-Dimension Bias Correction (RBC) and Simple Estimation (SE). They are applied to fuse the Multisensor Precipitation Estimator (MPE) and radar-only Next Generation QPE (Q2) products at the 15-min 1-km resolution (Experiment 1), and the MPE and Collaborative Adaptive Sensing of the Atmosphere (CASA) QPE products at the 15-min 500-m resolution (Experiment 2). The resulting fused estimates are evaluated using the 15-min rain gauge observations from the City of Grand Prairie in the Dallas–Fort Worth Metroplex (DFW) in north Texas. The main criterion used for evaluation is that the fused QPE improves over the ingredient QPEs at their native spatial resolutions, and that, at the higher resolution, the fused QPE improves not only over the ingredient higher-resolution QPE but also over the ingredient lower-resolution QPE trivially disaggregated using the ingredient high-resolution QPE. All four procedures assume that the ingredient QPEs are unbiased, which is not likely to hold true in reality even if real-time bias correction is in operation. To test robustness under more realistic conditions, the fusion procedures were evaluated with and without post hoc bias correction of the ingredient QPEs. The results show that only SE passes the evaluation criterion consistently. The performance of DE and BC are generally comparable; while DE is more attractive for computational economy, BC is more attractive for reducing occurrences of negative estimates. The performance of RBC is poor as it does not account for magnitude-dependent biases in the QPE products. SE assumes that the higher-resolution QPE product is skillful in capturing spatiotemporal variability of precipitation at its native resolution, and that the lower-resolution QPE product provides skill at its native resolution. While the above assumptions may not always be met, the simplicity and robustness observed in this work make SE an extremely attractive choice as a simple post-processor to the QPE process. Also, unlike the other procedures considered in this work, it is extremely easy to update the statistical parameters of SE in real time, similarly to the real-time bias correction currently used in MPE, for improved performance via self-learning.

Multiscale EnKF Assimilation of Radar and Conventional Observations and Ensemble Forecasting for a Tornadic Mesoscale Convective System

Abstract In recent studies, the authors have successfully demonstrated the ability of an ensemble Kalman filter (EnKF), assimilating real radar observations, to produce skillful analyses and subsequent ensemble-based probabilistic forecasts for a tornadic mesoscale convective system (MCS) that occurred over Oklahoma and Texas on 9 May 2007. The current study expands upon this prior work, performing experiments for this case on a larger domain using a nested-grid EnKF, which accounts for mesoscale uncertainties through the initial ensemble and lateral boundary condition perturbations. In these new experiments, conventional observations (including surface, wind profiler, and upper-air observations) are assimilated in addition to the WSR-88D and the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) radar data used in the previous studies, better representing meso- and convective-scale features. The relative impacts of conventional and radar data on analyses and forecasts are examined, and biases within the ensemble are investigated. The new experiments produce a substantially improved forecast, including better representation of the convective lines of the MCS. Assimilation of radar data substantially improves the ensemble precipitation forecast. Assimilation of conventional data together with radar observations substantially improves the forecast of near-surface mesovortices within the MCS, improves forecasts of surface temperature and dewpoint, and imparts a slight but noticeable improvement to short-term precipitation forecasts. Furthermore, ensemble analyses and forecasts are found to be sensitive to the localization radius applied to conventional data within the EnKF.

A Drop Size Distribution (DSD)-Based Model for Evaluating the Performance of Wet Radomes for Dual-Polarized Radars

AbstractA novel analytical method is presented for evaluating the electrical performance of a radome for a dual-polarized phased-array antenna under rain conditions. Attenuation, reflections, and induced cross polarization are evaluated for different rainfall conditions and radome types. The authors present a model for estimating the drop size distribution on a radome surface based on skin surface material, area, inclination, and rainfall rate. Then, a multilayer radome model based on the transmission-line-equivalent circuit model is used to characterize the radome’s scattering parameters. Numerical results are compared with radar data obtained in the Next Generation Weather Radar (NEXRAD) and Collaborative Adaptive Sensing of the Atmosphere (CASA) systems, and good agreement is found.

The Advantages of a Mixed-Band Radar Network for Severe Weather Operations: A Case Study of 13 May 2009

Abstract Adding a mix of X- or C-band radars to the current Weather Surveillance Radar-1988 Doppler (WSR-88D) network could address several limitations of the network, including improvements to spatial gaps in low-level coverage and temporal sampling of volume scans. These limitations can result in missing critical information in highly dynamic events, such as tornadoes and severe straight-line wind episodes. To evaluate the potential value of a mixed-band radar network for severe weather operations, a case study is examined using data from X- and S-band radars. On 13 May 2009, a thunderstorm complex associated with a cold front moved southward into southwest Oklahoma. A tornado rapidly developed from an embedded supercell within the complex. The life cycle of the tornado and subsequent wind event was sampled by the experimental Collaborative Adaptive Sensing of the Atmosphere (CASA) radar testbed of four X-band radars as well as two operational WSR-88Ds. In this study, the advantages of a mixed-band radar network are demonstrated through a chronological analysis of the event. The two radar networks provided enhanced overall situational awareness. Data from the WSR-88Ds provided 1) clear-air sensitivity, 2) a broad overview of the storm complex, 3) a large maximum unambiguous range, and 4) upper-level scans up to 19.5°. Data from the CASA radars provided 1) high-temporal, 1-min updates; 2) overlapping coverage for dual-Doppler analysis; and 3) dense low-level coverage. The combined system allowed for detailed, dual- and single-Doppler observations of a wind surge, a mesocyclone contraction, and a downburst.

A Variational Approach for Retrieving Raindrop Size Distribution from Polarimetric Radar Measurements in the Presence of Attenuation

AbstractThis study presents a two-dimensional variational approach to retrieving raindrop size distributions (DSDs) from polarimetric radar data in the presence of attenuation. A two-parameter DSD model, the constrained-gamma model, is used to represent rain DSDs. Three polarimetric radar measurements—reflectivity ZH, differential reflectivity ZDR, and specific differential phase KDP—are optimally used to correct for the attenuation and retrieve DSDs by taking into account measurement error effects. Retrieval results with simulated data demonstrate that the proposed algorithm performs well. Applications to real data collected by the X-band Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) radars and the C-band University of Oklahoma–Polarimetric Radar for Innovations in Meteorology and Engineering (OU-PRIME) also demonstrate the efficacy of this approach.

An Analysis of Vortices Embedded within a Quasi-Linear Convective System Using X-Band Polarimetric Radar

Abstract On 2 April 2010, a developing quasi-linear convective system (QLCS) moved rapidly northeastward through central Oklahoma spawning at least three intense, mesoscale vortices. At least two of these vortices caused damage rated as category 0 to 1 on the enhanced Fujita scale (EF0–EF1) in and near the town of Rush Springs. Two radar networks—the National Weather Service Weather Surveillance Radar-1988 Doppler network (WSR-88D) and the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) radar network—collected high spatial and temporal resolution data of the event. This study is an in-depth polarimetric analysis of mesovortices within a QLCS. In this case study, the storm development and evolution of the QLCS mesovortices are examined. Significant findings include the following: 1) The damage in Rush Springs was caused by a combination of the fast translation speed and the embedded circulations associated with QLCS vortices. The vortices’ relative winds nearly negated the storm motion to the left of the vortex, but doubled the ground-relative wind to the right of the vortex. 2) A significant differential reflectivity (ZDR) arc developed along the forward flank of the first vortex. The ZDR arc propagated northeastward along the QLCS with the development of each new vortex. 3) A minimum in the copolar correlation coefficient (ρhv) in the center of the strongest vortex was observed, indicating the likely existence of a polarimetric tornado debris signature (TDS). A secondary ρhv minimum also was found just to the right of the vortex center, possibly associated with lofted debris from straight-line winds.

The CASA quantitative precipitation estimation system: a five year validation study

Abstract. Flooding is one of the most common natural hazards that produce substantial loss of life and property. The QPE products that are derived at high spatiotemporal resolution, which is enabled by the deployment of a dense radar network, have the potential to improve the prediction of flash-flooding threats when coupled with hydrological models. The US National Science Foundation Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is dedicated to revolutionizing our ability to observe, understand, predict, and respond to hazardous weather events, especially in the lower atmosphere. CASA's technology enables precipitation observation close to the ground and QPE is one of the important products generated by the system. This paper describes the CASA QPE system built on the various underlying technologies of networked X-band radar systems providing high-resolution (in space and time) measurements, using the rainfall products from the radar. Evaluation of the networked rainfall product using 5 yr of data from the CASA IP-1 test bed is presented. Cross validation of the product using 5 yr of data with a gauge network is also provided. The validation shows the excellent performance of the CASA QPE system with a standard error of 25% and a low bias of 3.7%. Examples of various CASA rainfall products including instantaneous and hourly rainfall accumulations are shown.

Ensemble Probabilistic Forecasts of a Tornadic Mesoscale Convective System from Ensemble Kalman Filter Analyses Using WSR-88D and CASA Radar Data

AbstractThis study examines the ability of a storm-scale numerical weather prediction (NWP) model to predict precipitation and mesovortices within a tornadic mesoscale convective system that occurred over Oklahoma on 8–9 May 2007, when the model is initialized from ensemble Kalman filter (EnKF) analyses including data from four Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) X-band and five Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radars. Ensemble forecasts are performed and probabilistic forecast products generated, focusing on prediction of radar reflectivity (a proxy of quantitative precipitation) and mesovortices (an indication of tornado potential).Assimilating data from both the CASA and WSR-88D radars for the ensemble and using a mixed-microphysics ensemble during data assimilation produces the best probabilistic mesovortex forecast. The use of multiple microphysics schemes within the ensemble aims to address at least partially the model physics uncertainty and effectively plays a role of flow-dependent inflation (in precipitation regions) during EnKF data assimilation. The ensemble predicts with high probability (approximately 0.65) the near-surface mesovortex associated with the first of three reported tornadoes. Though a bias toward stronger precipitation is noted in the ensemble forecasts, all experiments produce skillful probabilistic forecasts of radar reflectivity on a 0–3-h time scale as evaluated by multiple probabilistic verification metrics. These results suggest that both the inclusion of CASA radar data and use of a mixed-microphysics ensemble during EnKF data assimilation positively impact the skill of 2–3-h ensemble forecasts of mesovortices, despite having little impact on the quality of precipitation forecasts (analyzed in terms of predicted radar reflectivity), and are important steps toward an operational EnKF-based ensemble analysis and probabilistic forecast system to support convective-scale warn-on-forecast operations.

Wideband Waveform Design Principles for Solid-State Weather Radars

Abstract The use of solid-state transmitters is becoming increasingly viable for atmospheric radars and is a key part of the strategy to realize any dense network of low-powered radars. However, solid-state transmitters have low peak powers and this necessitates the use of pulse compression waveforms. In this paper frequency diversity in a wideband waveform design is proposed to mitigate the low sensitivity of solid-state transmitters. In addition, the waveforms mitigate the range-eclipsing problem associated with long pulse compression. An analysis of the performance of pulse compression using mismatched compression filters designed to minimize sidelobe levels is presented. The impact of the range sidelobe level on the retrieval of Doppler moments is discussed. Realistic simulations are performed based on both the Colorado State University–University of Chicago–Illinois State Water Survey (CSU–CHILL) and the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) Integrated Project I (IP1) radar data.

Analysis of a Tornadic Mesoscale Convective Vortex Based on Ensemble Kalman Filter Assimilation of CASA X-Band and WSR-88D Radar Data

Abstract One of the goals of the National Science Foundation Engineering Research Center (ERC) for Collaborative Adaptive Sensing of the Atmosphere (CASA) is to improve storm-scale numerical weather prediction (NWP) by collecting data with a dense X-band radar network that provides high-resolution low-level coverage, and by assimilating such data into NWP models. During the first spring storm season after the deployment of four radars in the CASA Integrated Project-1 (IP-1) network in southwest Oklahoma, a tornadic mesoscale convective system (MCS) was captured by CASA and surrounding Weather Surveillance Radars-1988 Doppler (WSR-88Ds) on 8–9 May 2007. The MCS moved across northwest Texas and western and central Oklahoma; two tornadoes rated as category 1 on the enhanced Fujita scale (EF-1) and one tornado of EF-0 intensity were reported during the event, just to the north of the IP-1 network. This was the first tornadic convective system observed by CASA. To quantify the impacts of CASA radar data in storm-scale NWP, a set of data assimilation experiments were performed using the Advanced Regional Prediction System (ARPS) ensemble Kalman filter (EnKF) system configured with full model physics and high-resolution terrain. Data from four CASA IP-1 radars and five WSR-88Ds were assimilated in some of the experiments. The ensemble contained 40 members, and radar data were assimilated every 5 min for 1 h. While the assimilation of WSR-88D data alone was able to produce a reasonably accurate analysis of the convective system, assimilating CASA data in addition to WSR-88D data is found to improve the representation of storm-scale circulations, particularly in the lowest few kilometers of the atmosphere, as evidenced by analyses of gust front position and comparison of simulated Vr with observations. Assimilating CASA data decreased RMS innovation of the resulting ensemble mean analyses of Z, particularly in early assimilation cycles, suggesting that the addition of CASA data allowed the EnKF system to more quickly achieve a good result. Use of multiple microphysics schemes in the forecast ensemble was found to alleviate underdispersion by increasing the ensemble spread. This work is the first assimilating real CASA data into an NWP model using EnKF.

Impact of CASA Radar and Oklahoma Mesonet Data Assimilation on the Analysis and Prediction of Tornadic Mesovortices in an MCS

Abstract The impact of radar and Oklahoma Mesonet data assimilation on the prediction of mesovortices in a tornadic mesoscale convective system (MCS) is examined. The radar data come from the operational Weather Surveillance Radar-1988 Doppler (WSR-88D) and the Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere’s (CASA) IP-1 radar network. The Advanced Regional Prediction System (ARPS) model is employed to perform high-resolution predictions of an MCS and the associated cyclonic line-end vortex that spawned several tornadoes in central Oklahoma on 8–9 May 2007, while the ARPS three-dimensional variational data assimilation (3DVAR) system in combination with a complex cloud analysis package is used for the data analysis. A set of data assimilation and prediction experiments are performed on a 400-m resolution grid nested inside a 2-km grid, to examine the impact of radar data on the prediction of meso-γ-scale vortices (mesovortices). An 80-min assimilation window is used in radar data assimilation experiments. An additional set of experiments examines the impact of assimilating 5-min data from the Oklahoma Mesonet in addition to the radar data. Qualitative comparison with observations shows highly accurate forecasts of mesovortices up to 80 min in advance of their genesis are obtained when the low-level shear in advance of the gust front is effectively analyzed. Accurate analysis of the low-level shear profile relies on assimilating high-resolution low-level wind information. The most accurate analysis (and resulting prediction) is obtained in experiments that assimilate low-level radial velocity data from the CASA radars. Assimilation of 5-min observations from the Oklahoma Mesonet has a substantial positive impact on the analysis and forecast when high-resolution low-level wind observations from CASA are absent; when the low-level CASA wind data are assimilated, the impact of Mesonet data is smaller. Experiments that do not assimilate low-level wind data from CASA radars are unable to accurately resolve the low-level shear profile and gust front structure, precluding accurate prediction of mesovortex development.

Polarimetric Doppler Radar Observations of Kelvin–Helmholtz Waves in a Winter Storm

Abstract Kelvin–Helmholtz waves were observed by the Twin Lakes, Oklahoma (KTLX), Weather Surveillance Radar-1988 Doppler (WSR-88D); the Norman, Oklahoma (KOUN), polarimetric WSR-88D; and the polarimetric Collaborative Adaptive Sensing of the Atmosphere (CASA) radars on 30 November 2006 during a winter storm in central Oklahoma. The life cycle and structure of the waves are analyzed from the radar data, and the nearby atmospheric conditions are examined. The initial perturbations associated with the waves are first evident only in the radars’ radial velocity fields. As the waves mature, perturbations become discernable in the reflectivity factor Z and spectrum width (SW) fields of both radars, and in the differential reflectivity Zdr and, to a lesser extent, the cross-correlation coefficient ρhv fields of KOUN. As the waves break and begin to dissipate, the perturbations subside. A dual-Doppler analysis is synthesized to examine the kinematic structure of the waves and to relate the polarimetric observations to the kinematics. It is determined that Z and Zdr are enhanced in regions of upward motion (wave crests), and ρhv is reduced in the same vicinity and near the base of the wave circulations. Vertical velocity perturbations transport horizontal momentum upward and downward, inducing horizontal wind perturbations that are approximately 90° out of phase and downstream from their corresponding vertical velocity perturbations. Perturbations in Z, Zdr, and ρhv are observed in the vicinity of wave crests while SW perturbations occur predominately in and just upstream from wave troughs. It is determined that perturbations in the polarimetric variables are a result of the waves modifying local precipitation microphysics. Perturbations in Z and Zdr are hypothesized to be the result of columnar ice crystal generation whereas those in ρhv likely result from the mixing of ice crystals of various shapes and sizes. Perturbations in SW are a result of turbulent motions likely associated with wave breaking and downward advection of a strong shear layer.

On the scale-dependent propagation of hydrologic uncertainty using high-resolution X-band radar rainfall estimates

Radar precipitation estimates can improve hydrologic prediction over a range of spatial scales represented by both rural and urban basins. Flooding results from the combination of heavy precipitation and the distributed hydraulic and hydrologic characteristics of the basin. Accuracy and spatial scaling of radar estimated rainfall, and its impact at relevant hydrologic scales is an important determinant of hydrologic prediction accuracy and flood forecasting. Results of simulations using archival radar events are used to demonstrate the sources of uncertainty affecting site-specific flood forecasts within the distributed hydrologic model, Vflo. Radar data used in this analysis are derived from both S-band (NEXRAD/88D) and the Collaborative Adaptive Sensing of the Atmosphere (CASA), polarimetric X-band radars. X-band radars have the capability to provide higher spatial and temporal resolution than the conventional radars operating at S-band. However, compared to S-band, X-band radars have shorter wavelengths and suffer from attenuation, or even total extinction of the radar signal at short ranges from the radar. Degradation of precipitation mapping is a serious concern, especially in heavy precipitation over distances associated with watersheds prone to flooding. Compared to rain gauge accumulations, X-band radar polarimetric rainfall estimates were significantly degraded beyond about 15 km from the radars. With rainfall input derived from X-band radars, uncertainty in runoff volume scales with watershed area as a smooth monotonically decreasing function as area increases due to averaging of random errors in the input. Relative to estimates derived from S-band radar, unreliable hydrograph response was produced using X-band polarimetric rainfall estimates as input to a physics-based distributed hydrologic model, especially for watershed areas less than about 20 km 2.