Impact Of Radar Data Assimilation Research Articles

Impact of Radar Data Assimilation on the Simulation of a Heavy Rainfall Event Over Manaus in the Central Amazon

Impact of Radar Data Assimilation on the Simulation of a Heavy Rainfall Event Over Manaus in the Central Amazon

Impact of radar data assimilation on a squall line over the Yangtze–Huaihe River Basin with a radar reflectivity operator accounting for ice‐phase hydrometeors

AbstractA radar reflectivity observation operator with ice‐phase hydrometeors was employed for the assimilation of the real radar observations of a squall line on June 14, 2009, over Anhui and Jiangsu provinces in China. The three‐dimensional variational (3DVAR) data‐assimilation system along with the Weather Research and Forecasting (WRF) model was applied. The impact of assimilating radar radial velocity and reflectivity is investigated by analysing the meteorological conditions for different stages of the squall line. The results show that the radar data‐assimilation experiments adjust and improve the initial fields to some extent for the squall line with the cycling data‐assimilation strategy. The analysed and predicted radar composite reflectivity is significantly improved with the use of the ice‐phase hydrometeors observation operator. The improvements are illustrated in terms of the wind fields for both the surface and upper level, the radar reflectivity, the surface perturbation potential temperature and the surface perturbation pressure. In addition, the forecast skills of the precipitation are improved noticeably for both the location and the intensity after using the ice‐phase hydrometeors radar observation operator.

Impact of radar data assimilation on simulations of precipitable water with the Harmonie model: A case study over Cyprus

Understanding the mesoscale spatial and temporal variability of precipitation (of the order of 1 to 100 km, and of minutes to hours, respectively) is essential for larger-scale studies, especially in highly heterogeneous areas, such as coastal regions, mountains and urban areas. The adequate capturing of the precipitation patterns in both diagnostic and prognostic terms remains an open challenge. On the one hand, commonly used ground measuring devices, such as rain gauges, disdrometers and other point instruments, exhibit an ineffective sampling for the whole range of scales and areas covered with precipitation. On the other hand, weather radars provide a much greater spatial and temporal resolution which, however, must be exploited with care in rain rate estimations, due to complicated and non-unique transfer functions used in the processing procedures. This study aims at bridging, to at least some extent, this scale gap and to demonstrate the role of fine-scale precipitation estimates derived from weather radar data, and to illustrate their impacts on larger scale weather patterns. The focus is on the precipitable water content and its changes during a Mediterranean cyclone evolution.In this paper, the first results with the regional radar signal processing chain that provides the radar data assimilation (RDA) in the Harmonie convection permitting numerical model are described. The numerical experiments exhibit effective simulation of the precipitable water at different stages of the frontal depression which affected Cyprus in the Eastern Mediterranean, during 31 December 2018–2 January 2019. In particular, it was found that the major volume of precipitable water was related to the cyclone frontal zone (which is in turn related to heavier precipitation), followed by a weaker field of precipitable water behind the frontal zone (and weaker precipitation). During the whole severe weather period, the precipitable water minimum was maintained over the island's central mountain range, reflecting the importance of interactions and feedbacks between the air flow and orography.The radar data assimilation influences the results of the simulations, depending on the particular area and cyclone stage. The largest innovations due to RDA occur with the approach of the cyclone's frontal zone. Enhancement of precipitable water due to RDA is observed over most of the whole area, but this is more pronounced in weaker precipitation locations. The impact is associated with a shift of the precipitable water maximum towards higher values in the precipitable water distribution, which is accompanied by enlarged sizes of areas with greater precipitable water. During the cyclone's mature stage and at the rear zone, the integrated RDA impact over the whole domain is almost negligible but takes the form of mesoscale cells of opposite signs; at this stage, radar measurements yield an alteration of both the positions and rates of spatial precipitable water fields, which result in intermittent mesoscale changes.

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

AbstractWe 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.

Impact of radar data assimilation and orography on predictability of deep convection

Deep convection represents a classic example of limited predictability on the convective scale. We investigate the potential impact of assimilating radar reflectivity and velocity observations on the predictive skill of precipitation in short‐term forecasts (up to 6 hr) using the operational COSMO‐KENDA ensemble data assimilation and forecasting system in an idealized set‐up. Additionally, the role of a Gaussian‐shaped mountain providing a permanent source of predictability for the location of convective precipitation is examined with and without data assimilation.Using a hierarchy of quality measures, we found a long‐lasting beneficial impact of radar data assimilation throughout the entire forecast range of 6 hr. The up‐scaled normalized RMS error and the Fractions Skill Score show that precipitation forecasts based on initial conditions including the assimilation of radar data are skilful on scales larger than 40 km at a lead time of 6 hr and thus are better than a reference ensemble without any data assimilation at lead times of less than 1 hr. The presence of orography strongly increases the predictability of precipitation throughout the forecast range, particularly within the immediate area and where no radar data are assimilated.This remarkable impact of radar data assimilation exceeding 6 hr is larger and longer‐lasting than in many real modelling systems. While this is partly related to the idealized set‐up assuming a perfect forecast model, perfect large‐scale boundary conditions and a perfect radar forward operator, our study demonstrates the potential impact that could be achieved for radar data assimilation if the systematic model and operator deficiencies, as well as boundary condition errors, could be reduced. Furthermore, our results highlight the important role of orography in structuring the precipitation field, especially if no observations are assimilated.

Assimilating Doppler radar observations with an ensemble Kalman filter for convection-permitting prediction of convective development in a heavy rainfall event during the pre-summer rainy season of south China

This study examines the effectiveness of an ensemble Kalman filter based on the weather research and forecasting model to assimilate Doppler-radar radial-velocity observations for convection-permitting prediction of convection evolution in a high-impact heavy-rainfall event over coastal areas of South China during the pre-summer rainy season. An ensemble of 40 deterministic forecast experiments (40 DADF) with data assimilation (DA) is conducted, in which the DA starts at the same time but lasts for different time spans (up to 2 h) and with different time intervals of 6, 12, 24, and 30 min. The reference experiment is conducted without DA (NODA).To show more clearly the impact of radar DA on mesoscale convective system (MCS) forecasts, two sets of 60-member ensemble experiments (NODA EF and exp37 EF) are performed using the same 60-member perturbed-ensemble initial fields but with the radar DA being conducted every 6 min in the exp37 EF experiments from 0200 to 0400 BST. It is found that the DA experiments generally improve the convection prediction. The 40 DADF experiments can forecast a heavy-rain-producing MCS over land and an MCS over the ocean with high probability, despite slight displacement errors. The exp37 EF improves the probability forecast of inland and offshore MCSs more than does NODA EF. Compared with the experiments using the longer DA time intervals, assimilating the radial-velocity observations at 6-min intervals tends to produce better forecasts. The experiment with the longest DA time span and shortest time interval shows the best performance. However, a shorter DA time interval (e.g., 12 min) or a longer DA time span does not always help. The experiment with the shortest DA time interval and maximum DA window shows the best performance, as it corrects errors in the simulated convection evolution over both the inland and offshore areas. An improved representation of the initial state leads to dynamic and thermodynamic conditions that are more conducive to earlier initiation of the inland MCS and longer maintenance of the offshore MCS.

The impact of radar data assimilation on atmosphere state analysis in the Moscow region

The results of numerical experiments with the data assimilation system including the WRF-ARW mesoscale atmospheric model and WRFDA analysis package in the 3D-Var mode are considered. The focus is on the impact of Doppler weather radar data on the quality of short-range weather forecasting. The maps of weather events and cloud top constructed from the modeling by GIMET-2010 software package are analyzed using the web-GIS METEORAD. The experiments in the Moscow region demonstrated that if only radial wind is assimilated, the intensity of simulated cloud and precipitation formation is the closest to the observed one; however, the spatial mismatches of simulated and observed cloud systems are possible. If Doppler radar data on radial wind and reflectivity are assimilated, the general pattern and location of clouds and precipitation are simulated more accurately, but there is a certain overestimation of convection intensity which leads to the overestimation of the number of thunderstorms and rainfall rate.

Short-Term Probabilistic Forecasts of the 31 May 2013 Oklahoma Tornado and Flash Flood Event Using a Continuous-Update-Cycle Storm-Scale Ensemble System

AbstractA continuous-update-cycle storm-scale ensemble data assimilation (DA) and prediction system using the ARW model and DART software is used to generate retrospective 0–6-h ensemble forecasts of the 31 May 2013 tornado and flash flood event over central Oklahoma, with a focus on the prediction of heavy rainfall. Results indicate that the model-predicted probabilities of strong low-level mesocyclones correspond well with the locations of observed mesocyclones and with the observed damage track. The ensemble-mean quantitative precipitation forecast (QPF) from the radar DA experiments match NCEP’s stage IV analyses reasonably well in terms of location and amount of rainfall, particularly during the 0–3-h forecast period. In contrast, significant displacement errors and lower rainfall totals are evident in a control experiment that withholds radar data during the DA. The ensemble-derived probabilistic QPF (PQPF) from the radar DA experiment is more skillful than the PQPF from the no_radar experiment, based on visual inspection and probabilistic verification metrics. A novel object-based storm-tracking algorithm provides additional insight, suggesting that explicit assimilation and 1–2-h prediction of the dominant supercell is remarkably skillful in the radar experiment. The skill in both experiments is substantially higher during the 0–3-h forecast period than in the 3–6-h period. Furthermore, the difference in skill between the two forecasts decreases sharply during the latter period, indicating that the impact of radar DA is greatest during early forecast hours. Overall, the results demonstrate the potential for a frequently updated, high-resolution ensemble system to extend probabilistic low-level mesocyclone and flash flood forecast lead times and improve accuracy of convective precipitation nowcasting.

Impact of Radar Data Assimilation on the Numerical Simulation of a Severe Storm in Croatia

Impact of Radar Data Assimilation on the Numerical Simulation of a Severe Storm in Croatia

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

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.

Prediction of Convective Storms at Convection-Resolving 1 km Resolution over Continental United States with Radar Data Assimilation: An Example Case of 26 May 2008 and Precipitation Forecasts from Spring 2009

For the first time ever, convection-resolving forecasts at 1 km grid spacing were produced in realtime in spring 2009 by the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma. The forecasts assimilated both radial velocity and reflectivity data from all operational WSR-88D radars within a domain covering most of the continental United States. In preparation for the realtime forecasts, 1 km forecast tests were carried out using a case from spring 2008 and the forecasts with and without assimilating radar data are compared with corresponding 4 km forecasts produced in realtime. Significant positive impact of radar data assimilation is found to last at least 24 hours. The 1 km grid produced a more accurate forecast of organized convection, especially in structure and intensity details. It successfully predicted an isolated severe-weather-producing storm nearly 24 hours into the forecast, which all ten members of the 4 km real time ensemble forecasts failed to predict. This case, together with all available forecasts from 2009 CAPS realtime forecasts, provides evidence of the value of both convection-resolving 1 km grid and radar data assimilation for severe weather prediction for up to 24 hours.

Radar data assimilation for the simulation of mesoscale convective systems

A heavy rainfall case related to Mesoscale Convective Systems (MCSs) over the Korean Peninsula was selected to investigate the impact of radar data assimilation on a heavy rainfall forecast. The Weather Research and Forecasting (WRF) three-dimensional variational (3DVAR) data assimilation system with tuning of the length scale of the background error covariance and observation error parameters was used to assimilate radar radial velocity and reflectivity data. The radar data used in the assimilation experiments were preprocessed using quality-control procedures and interpolated/thinned into Cartesian coordinates by the SPRINT/CEDRIC packages. Sensitivity experiments were carried out in order to determine the optimal values of the assimilation window length and the update frequency used for the rapid update cycle and incremental analysis update experiments.

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

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.

Improving Short-Term Storm Predictions by Assimilating both Radar Radial-Wind and Reflectivity Observations

Abstract A high-resolution data assimilation system is under development at the Naval Research Laboratory (NRL). The objective of this development is to assimilate high-resolution data, especially those from Doppler radars, into the U.S. Navy’s Coupled Ocean–Atmosphere Mesoscale Prediction System to improve the model’s capability and accuracy in short-term (0–6 h) prediction of hazardous weather for nowcasting. A variational approach is used in this system to assimilate the radar observations into the model. The system is upgraded in this study with new capabilities to assimilate not only the radar radial-wind data but also reflectivity data. Two storm cases are selected to test the upgraded system and to study the impact of radar data assimilation on model forecasts. Results from the data assimilation experiments show significant improvements in storm prediction especially when both radar radial-wind and reflectivity observations are assimilated and the analysis incremental fields are adequately constrained by the model’s dynamics and properly adjusted to satisfy the model’s thermodynamical balance.