Radar Radial Velocity Research Articles

Influence of Assimilation of NEXRAD-Derived 2D Inner-Core Structure Data from Single Radar on Numerical Simulations of Hurricane Charley (2004) near Its Landfall

This study presents the first research that assimilates the ground-based NEXRAD observations-derived two-dimensional (2D), azimuthally averaged radar radial velocity and reflectivity within 60 km of radius from the hurricane center to examine their influence on the analysis and prediction of a hurricane near and after its landfall. The mesoscale community Weather Research and Forecasting (WRF) model and its four-dimensional variational (4D-VAR) data assimilation system are utilized to conduct data assimilation experiments for Hurricane Charley (2004). Results show that assimilation of the radar inner-core data leads to better forecasts of hurricane tracks, intensity, and precipitation. The improved forecast outcomes imply that the changes in dynamical, thermal, and moisture structures from data assimilations made more reasonable conditions for the hurricane development near and after its landfall. Overall results indicate that the assimilation of the radar-derived 2D inner-core structure could be a feasible way to utilize the radar data for improved hurricane prediction.

An RTK UAV-Based Method for Radial Velocity Validation of Weather Radar

The quality of weather radar affects the reliability and effectiveness of monitoring severe convective weather. Therefore, rigorous calibration and validation are the foundation for the quantitative application of weather radar. Among the available methods, radial velocity validation is of great significance for reducing the false alarm rate in the identification of tornadoes and thunderstorms. Based on the traditional method that utilizes internal and external instrument radar velocity measurements, we propose a weather radar radial velocity validation method that uses RTK UAV to simulate external targets. In addition, according to the characteristics of the UAV application scenarios, we introduce the evaluation parameter of optimal absolute accuracy to supplement the original parametric system. The experimental results show that the evaluation parameter of optimal absolute accuracy can effectively reduce the interference caused by the systematic deviation of the UAV due to the internal and external environment, which can affect the validation results. When the UAV velocity is not greater than 10 m/s, the optimal absolute accuracy of the radial velocity validation is less than 0.05 m/s, which is essentially consistent with the external instruments’ measurement results. This method can be effectively applied to the procedural handling of weather radar radial velocity validation. It is significant for ensuring the accuracy and quality of weather radar radial velocity measurements and improving the effectiveness of radar velocity data applications.

Optimal Use of Radar Radial Winds in the HARMONIE Numerical Weather Prediction System

Abstract The use of radial velocity information from the European weather radar network is a challenging task, because of a heterogeneous radar network and the different ways of providing the Doppler velocity information. Preprocessing is therefore needed to harmonize the data. Radar observations consist of a very high resolution dataset, which means that it is both demanding to process as well as that the inherent resolution is much higher than the model resolution. One way of reducing the number of data is to create “super observations” (SO) by averaging observations in a predefined area. This paper describes the preprocessing necessary to use radar radial velocities in the data assimilation where the SO construction is included. Our main focus is to optimize the use of radial velocities in the HARMONIE–AROME numerical weather model. Several experiments were run to find the best settings for first-guess check limits as well as a tuning of the observation error value. The optimal size of the SO and the corresponding thinning distance for radar radial velocities was also studied. It was found that the radial velocity information and the reflectivity from weather radars can be treated differently when it comes to the size of the SO and the thinning. A positive impact was found when adding the velocities together with the reflectivity using the same SO size and thinning distance, but the best results were found when the SO and thinning distance for the radial velocities are smaller than the corresponding values for reflectivity.

The Impact of Radar Radial Velocity Data Assimilation Using WRF-3DVAR System with Different Background Error Length Scales on the Forecast of Super Typhoon Lekima (2019)

This study explores the impact of assimilating radar radial velocity (RV) on the forecast of Super Typhoon Lekima (2019) using the Weather Research and Forecasting (WRF) model and three-dimensional variational (3DVAR) assimilation system with different background error length scales. The results of two single observation tests show that the smaller background error length scale is able to constrain the spread of radar observation information within a relatively reasonable range compared with the larger length scale. During the five data assimilation cycles, the position and structure of the near-land typhoon are found to be significantly affected by the setting of the background error length scale. With a reduced length scale, the WRF-3DVAR system could effectively assimilate the radar RV to produce more accurate analyses, resulting in an enhanced typhoon vortex with a dynamic and thermal balance. In the forecast fields, the experiment with a smaller length scale not only reduces the averaged track error for the 24-h forecasts to less than 20 km, but it also more accurately captures the evolutions of the typhoon vortex and rainband during typhoon landing. In addition, the spatial distribution and intensity of heavy precipitation are corrected. For the 24-h quantitative precipitation forecasts, the equitable threat scores of the experiment with a reduced length scale are greater than 0.4 for the threshold from 1 to 100 mm and not less than 0.2 until the threshold increases to 240 mm. The enhanced prediction performances are probably due to the improved TC analysis.

Comparison of different momentum control variables on assimilating radar observations for the forecasts of a dispersive convective event

In this study, the effects of background error covariance (BE) using the stream function ψ and unbalanced velocity potential χu as momentum control variables (CV5 scheme) and BE using the velocity U and V as momentum control variables (CV7 scheme) on assimilating radar radial velocity and reflectivity data for short-term forecasts of dispersive convection in a weak environmental field are explored based on the weather research and forecasting model (WRF) model and its 3DVAR assimilation system. The 4 km resolution forecast samples are generated to formulate the CV5 and CV7 BE by the National Meteorological Center (NMC) method. The single-observation experiments reveal that the differences between the two BE statistics are mainly reflected on the momentum control variables. The increment of wind field from CV7 shows more small-scale local characteristics. Comparing with control experiment, real radar observation assimilation tests of CV5 and CV7 both improve the reflectivity and precipitation forecasts. But the CV7 scheme improves the forecasting of strong convective systems in weak environmental fields better than CV5. First, the CV7 scheme improves both reflectivity and dispersive precipitation forecasts and significantly suppresses the spurious precipitation forecasts when compared with the CV5 scheme. In addition, CV7 also significantly reduces the forecast errors of surface variables and the wind analysis from CV7 is more local. Further analysis shows that the CV7 improves the water vapor convergence conditions compared to the CV5 scheme, which may be the reason for its better performance in the subsequent forecasts.

Application of Radar Radial Velocity Data Assimilation in the Forecasts of Typhoon Linfa Based on Different Horizontal Length Scale Factors

In order to explore the improvement of radar radial velocity data assimilation on the initial and forecast fields of typhoons, this study assimilates the quality-controlled radial velocity data in the case of Typhoon Linfa (2015) using the three-dimensional variational data assimilation system of the weather research and forecasting model (WRF-3DVAR), and then conducts several sensitivity experiments with different horizontal length scale factors. The results show that reducing the horizontal length scale factor of the background error covariance can effectively assimilate the micro- and meso-scale information from radar data and improve the forecasting effect of Linfa. Following the optimization of the horizontal length scale factor, the radial velocity data assimilation can improve the typhoon wind field structure, produce reasonable cyclonic wind field increments, and further improve the dynamic and thermal structure of the inner core area of the typhoon. Then, we can obtain a better initial field of model forecasting, and thus typhoon track and intensity forecasting are improved.

Application of Radar Radial Velocity Data Assimilation Based on Different Momentum Control Variables in Forecasting Typhoon Kompasu

The study is based on the Weather Research and Forecasting Model (WRF) and the three-dimensional variational (3DVAR) data assimilation system. Based on two different control variables, the effects of radar radial velocity assimilation in forecasting of the tropical cyclone (TC) Kompasu were evaluated. The single observation experiment showed that DA_ψχ produces cyclonic increments, while DA_UV only produces increments in the same direction as the observation. DA_ψχ significantly enhances the wind field at 850 hPa with a large number of unphysical cyclonic increments. On the other hand, DA_UV produces reasonable cyclonic increments to enhance the TC. The assimilation of DA_UV makes the surface wind enhanced and the sea level pressure at the TC center reduced. The circular structure of the DA_ψχ wind field is not clear and neither is the large wind area concentrated. In addition, the DA_ψχ shows spurious convection at the high altitude of the vertical cross section, while the DA_UV presents enhanced large wind area at the bottom. The RMSE of the radial velocity is smaller during the circular assimilation in DA_UV. DA_ψχ does not improve the track forecast of Kompasu, while DA_UV shows a significant improvement by the track forecast.

Combined assimilation of radar and lightning data for the short-term forecast of severe convection system

In this study, a lightning data assimilation (LDA) method adjusting dynamical fields was examined in the rapid cycle with Weather Research and Forecasting (WRF) model and WRF Data Assimilation (WRFDA). This method retrieves pseudo vertical velocity profiles from total lightning observations and promotes wind convergence over lightning regions. This newly developed LDA scheme is compared with radar data assimilation (RDA) that has been routinely applied in severe storm nowcasting models. Depending on whether the radar or lightning data is assimilated, four experiments were designed to evaluate the positive impacts of dynamical adjustment from LDA. Using a typical severe mesoscale convection system occurred in Beijing, we found that the effect of RDA and LDA are different. The assimilation of radar radial velocity mainly improves the forecast in a longer time, while assimilation of pseudo-vertical-velocity from lightning data mainly corrects intensity and location of the forecasted precipitation. When both radar and lightning data are assimilated, the small-scale wind convergence are promoted and contributes to the intensified updrafts. Thermal and water vapor fields are also adjusted indirectly. Consequently, the convective precipitation is significantly improved and the positive impacts from the combined assimilation scheme persisted over a longer period (at least 3 h). It can be concluded that a combined assimilation of convective data from multiple sources such as radar and lightning data enhance prominently the accuracy of short-term convection forecasts.

Assimilation of Water Vapor Retrieved from Radar Reflectivity Data through the Bayesian Method

This work describes the implementation of an updated radar reflectivity assimilation scheme with the three-dimensional variational (3D-Var) system of Weather Research and Forecast (WRF). The updated scheme, instead of the original scheme assuming the relative humidity to a fixed value where radar reflectivity is higher than a threshold, assimilates pseudo water vapor retrieved by the Bayesian method, which would be consistent with clouds/precipitations provided by the model in theory. To verify the effect of the updated scheme to the improvement of precipitation simulation, a convective case in Wenquan County and the continuous monthly simulation with contrasting experiments in Xinjiang were performed. The test of single reflectivity observation demonstrates that the water vapor retrieved by the Bayesian method is consistent with the meteorological situation around. In the convective case, both the updated and original scheme results show that the assimilation of pseudo water vapor can adjust to the environmental conditions of water vapor and temperature. This can improve the hourly precipitation forecast skill more than the contrasting experiment, which was designed to only assimilate conventional observations and radar radial velocity data. In the continuous monthly experiments, the updated scheme reveals that the analysis of water vapor is more reasonable, and obtains a better precipitation forecast skill for 6 h accumulated precipitation than the contrasting experiments.

Evaluate Radar Data Assimilation in Two Momentum Control Variables and the Effect on the Forecast of Southwest China Vortex Precipitation

Based on the Weather Research and Forecasting (WRF) Model and the three-dimensional variational (3DVAR) data assimilation system, this study investigates the effects of assimilation of radar reflectivity and radial velocity under different momentum control variables on the forecast of Southwest China Vortex precipitation. It is shown that the U−V control variable strengthens the wind speed and vorticity to be better matching the observation, while using ψ−χ as the control variable will produce too large increments which are unphysical. The root mean square errors (RMSE) of radar radial velocity are around 2.4 m/s in the experiment using ψ−χ control variables, while the RMSE are below 2 m/s in the experiment with U−V control variables. The composite reflectivity from the analysis of the U−V control variables matches better with the observation than that from the analysis of the ψ−χ control variables, i.e., the forecast rain band location under U−V control variables is more accurate. ψ−χ control variable enhances the cold high-pressure system in near surface, while the U−V control variable is not significant. The water vapor flux convergence in the lower layers of the ψ−χ control variable is overestimated leading excessive precipitation in the forecast. The Equitable Threat Score (ETS) of the U−V control variable is about 0.1 higher than ψ−χ control variable. In summary, the U−V control variable is superior to the ψ−χ control variable in terms of analysis and forecasting about Southwest China Vortex precipitation.

Gulf Stream Position, Width, and Orientation Estimated from HF Radar Radial Velocity Maps off Cape Hatteras, North Carolina

Abstract A method to extract characteristics of the Gulf Stream (GS) surface flow field using high-frequency radar (HFR)–derived currents is described. Radial velocity measurements, from radar installations near Cape Hatteras, North Carolina, serve as input, chosen because of the greater spatial and temporal coverage provided compared to total velocity fields. The landward GS edge, jet axis, orientation, and cyclonic shear zone (CSZ) width are identified along bearings within the radar footprint. The method is applied to observations from two radar installations from November 2014 and provides GS estimates with daily temporal resolution. Results along eight bearings provide a consistent representation of GS variability dominated by the passage of meanders. Average distance to the GS edge along bearings varies from 50 to 100 km; distance estimate quality degrades with range from the radars. Monthly mean GS jet axis locations from satellite sea surface height (SSH) and the algorithm are consistent. Cross correlations between estimates of GS characteristics in the same region vary from 0.37 to 0.73 for the GS edge. Estimates of radar distance to the GS edge are negatively correlated with current velocity measurements nearest the surface from a moored 150-kHz acoustic Doppler current profiler and vary between −0.58 and −0.71. GS CSZ width metrics range from mean values of 29–31 km. Daily GS orientation estimates are affected by the crossing angle of the radial bearing relative to the GS. Lags from the cross correlations of monthly mean properties suggest meander propagation speed estimates increase from 43.2 km day−1 south of the cape, to 136.8 km day−1 just east of it.

Automated Recognition of Macro Downburst Using Doppler Weather Radar

In light of the macro downburst’s ground divergent flow field characteristics and high reflectivity, this paper proposes an algorithm for identifying the downburst area using a Doppler weather radar low-level radial velocity and reflectivity factor (abbreviated as reflectivity, the same below). To binarize the radial velocity, perform quality control on the radial velocity and reflectivity, then combine the reflectivity and the radial velocity threshold. Following that, use the Eight-Neighborhood method to retrieve the positive and negative velocity connected regions and perform the connected regions. The positive and negative velocity pairs are then matched, and the zero Doppler velocity line between the positive and negative velocity pairs is extracted, followed by the center recognition of the positive and negative velocity downburst areas. The data of downbursts detected by Doppler radar in Jinan, Shandong Province, are used for algorithm verification in this paper. The results show that the proposed algorithm can detect the macro downburst area and identify the downburst center.

Impact of Combined Assimilation of Wind Profiler and Doppler Radar Data on a Convective-Scale Cycling Forecasting System

Abstract The two types of wind observations, profiler and radar radial velocity, have been successfully assimilated into numerical weather prediction (NWP) systems. However, the added value of profiler data, especially from a densely deployed profiler network, is unknown when assimilated together with Doppler radar radial velocity. In this article, two combined assimilation strategies of profilers along with radar radial winds are compared within a convective-scale data assimilation (DA) framework. In strategy I, the profiler data are assimilated with conventional observations to generate an intermediate analysis that acts as a prior for radar data assimilation. In strategy II, both profiler and radar data are considered as storm-scale and assimilated within the same pass. Single- and dual-observation assimilation experiments indicate that for strategy I, the profiler DA improvement can be partly canceled by the potentially negative impact of the assimilation of single-radar radial velocity afterward, particularly when the radial wind is nearly orthogonal to the prevailing wind. For strategy II, important complements are provided when profilers are assimilated within the same pass along with radial winds. The diagnostics for a low-level jet case demonstrate that both strategies facilitate improved analyses and forecasts. But strategy II may bring more moderate analysis increments, which indicate mutual constraints of the profiler and radial winds when assimilated within the same pass. The results obtained in 1-month, retrospective cycling experiments also show that the strategy II outperforms the strategy I with slightly better wind and precipitation forecasts. Significance Statement Due to the high spatial–temporal wind information provided by profiler and radar radial velocity measurements, their combined assimilation would be expected to improve wind analysis. To fully utilize dense profiler data and radar radial wind in future operational applications, this study proposes a suitable assimilation strategy. If the profilers are defined as synoptic-scale observations, the profiler and Doppler radar data must be assimilated in different passes to adopt different length and variance scales. Whereas it is more reasonable to use a small background correlation length consistent with the radial velocity and, therefore, assimilate in the same pass if the profiler data are considered to better sample storm-scale features. Single- and dual-observation experiments indicate that profiler data provide important complements, while the assimilation of single-radar radial wind may yield analyzed wind results that do not depict the ground truth. A low-level jet case and a 1-month impact study further show that the combined assimilation strategy of assimilating both profiler and Doppler radar using smaller background correlation lengths enhances the analysis and forecasting of wind, resulting in more accurate accumulated precipitation forecasts.

Evaluation and Modification of Microphysics Schemes on the Cold Pool Evolution for a Simulated Bow Echo in Southeast China

AbstractA merger‐formation bow echo (MFBE) in southeast China during the pre‐summer rainy season (PSRS) was simulated using three microphysics schemes including Thompson (THOM), Morrison (MORR), and Weather Research and Forecasting Double‐Moment 6‐Class (WDM6) schemes to compare against polarimetric observations and Variational Doppler Radar Analysis System (VDRAS) analyses. The three schemes captured the basic kinematic structures for this MFBE event after assimilating radar radial velocities, but all underpredicted the cold pool strength by ∼25%. Particularly, THOM produced the best raindrop size distributions (DSDs) and precipitation pattern compared with observed retrievals; further analyses indicated the larger raindrop size bias and the weak cold pool strength in THOM were owing to the relatively low rain breakup efficiency and inefficient rain evaporation, respectively. By decreasing the cutoff diameter of rain breakup parameterization from the default 1.6–1.2 mm (i.e., increasing breakup efficiency) and increasing evaporation efficiency by threefold in THOM, the simulated DSDs and precipitation were greatly improved, and the cold pool strength was significantly increased from 77% to 99% compared to that in VDRAS analyses. This study illustrated a plausible approach of combining polarimetric radar retrievals and VDRAS analyses as bases to adjust THOM default settings in simulating a MFBE event in southeast China with physical characteristics more consistent with observations. Since microphysical processes vary from convective organizations and climate regions, it is recognized more cases studies are needed in the future to examine the validity and approach in this study to improve simulations and predictions of MFBEs in southeast China.

Eight scenarios rapid update cycle effect on weather radar data assimilation WRF toward rainfall prediction in Palembang (cases study of flood events on November 12th, 2018)

Weather Research and Forecasting (WRF) is an open source numerical weather prediction model that can be used for high resolution rainfall predictions. Besides these advantages, WRF output accuracy can be affected by the initial condition. The accuration of WRF model can be improved by data assimilation. Data assimilation is combining observation data with model data to improve the initial state of atmospheric flow. This study aims to investigate the effect of assimilation weather radar in models using WRF for predictions rainfall events in Palembang region on November 12th, 2018. This study uses radar radial velocity data as input data for assimilation. The assimilation technique uses the 3DVAR with rapid update cycle (RUC) procedure 1 hour, 3 hours, 6 hours with spin up 12 and 6 hours. The output of the model verified using Global Satellite Mapping of Precipitation (GSMaP) data and using rain gauge data for point verification. The results of this study indicate that the output of the assimilation model, especially in the spin-up 12 hours skenario implementation of the 1-hour RUC is better than the model without assimilation. From the eight scenario models implemented, it can be concluded that the 12 hours spin up is better than the 6 hours spin up.

Assimilation of X‐Band Phased‐Array Radar Data With EnKF for the Analysis and Warning Forecast of a Tornadic Storm

AbstractThe impact of assimilating China's operational X‐band Phased‐Array radar's (X‐PAR) data on the analysis and warning forecast of the vortex structure and intensity of the June 8, 2018 Foshan, Guangdong province, tornadic storm was investigated for the first time using an Ensemble Kalman Filter (EnKF) data assimilation system. Both radar radial velocity (Vr) and reflectivity (Z) from two S‐band operational radars and one X‐PAR were assimilated. Deterministic forecasts were launched every 6 min from 05:42 UTC (20 min before the tornado touched down) to 06:00 UTC from the EnKF mean analysis field. Five experiments were conducted to examine the added capability of Z assimilation of the EnKF system, and to investigate the impact of assimilating X‐PAR data on the analysis and prediction of the tornadic storm. Compared to the experiment without Z assimilation, the assimilation of Z reduced the analysis error and greatly reduced the forecast error of Z. The assimilation of X‐PAR data greatly improved the vortex structure of the tornadic storm at low levels, and improved the intensity of the rear inflow of the tornadic storm, especially with a higher assimilation frequency. Compared to the experiments without X‐PAR data assimilation, assimilating X‐PAR data improved the predictability of tornadic storm.

Assimilation of Polarimetric Radar Data in Simulation of a Supercell Storm with a Variational Approach and the WRF Model

Polarimetric radar data (PRD) have potential to be used in numerical weather prediction (NWP) models to improve convective-scale weather forecasts. However, thus far only a few studies have been undertaken in this research direction. To assimilate PRD in NWP models, a forward operator, also called a PRD simulator, is needed to establish the relation between model physics parameters and polarimetric radar variables. Such a forward operator needs to be accurate enough to make quantitative comparisons between radar observations and model output feasible, and to be computationally efficient so that these observations can be easily incorporated into a data assimilation (DA) scheme. To address this concern, a set of parameterized PRD simulators for the horizontal reflectivity, differential reflectivity, specific differential phase, and cross-correlation coefficient were developed. In this study, we have tested the performance of these new operators in a variational DA system. Firstly, the tangent linear and adjoint (TL/AD) models for these PRD simulators have been developed and checked for the validity. Then, both the forward operator and its adjoint model have been built into the three-dimensional variational (3DVAR) system. Finally, some preliminary DA experiments have been performed with an idealized supercell storm. It is found that the assimilation of PRD, including differential reflectivity and specific differential phase, in addition to radar radial velocity and horizontal reflectivity, can enhance the accuracy of both initial conditions for model hydrometer state variables and ensuing model forecasts. The usefulness of the cross-correlation coefficient is very limited in terms of improving convective-scale data analysis and NWP.

Ice in Southern Ocean Clouds With Cloud Top Temperatures Exceeding −5°C

AbstractThis study documents the presence of ice in stratocumulus clouds with cloud top temperatures (CTT) > −5 °C in the cold sector of extratropical cyclones over the Southern Ocean (SO) during ten SO Clouds, Radiation, Aerosol Transport Experimental Study (SOCRATES) research flights. Case studies are presented showing ice signatures within clouds when CTT were between −2 and −5°C, evidenced in Doppler radar radial velocity changes observed during high‐altitude flight legs as ice particles melted across the 0°C isotherm. Ice on these legs was found to contribute to precipitation 3.8% of the time from clouds with −5°C < CTT <0°C. Clouds observed with a distinct melting level on high‐altitude flight legs overall had greater cloud depths, tops with higher reflectivities, and higher linear depolarization ratios, compared to clouds without a melting level. In situ flight legs were also analyzed when Himawari‐8 CTT were between 0 and −5°C and the aircraft was sampling in cloud within that temperature range. It was found that 3% of clouds sampled in situ with −5°C < CTT <0°C were mixed phase with a mean number concentration of 2.35 L−1 for nonspherical particles with maximum diameters >100 μm and 1.13 L−1 for nonspherical particles with maximum diameters >200 μm.

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

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.

Improving the Analysis and Forecast of Hurricane Dorian (2019) with Simultaneous Assimilation of GOES-16 All-Sky Infrared Brightness Temperatures and Tail Doppler Radar Radial Velocities

AbstractRecent studies have shown that the assimilation of all-sky infrared (IR) observations can be beneficial for tropical cyclone analyses and predictions. The assimilation of Tail Doppler Radar (TDR) radial velocity observations has also been shown to improve tropical cyclone analyses and predictions; however, there is a paucity of literature on the impacts of simultaneously assimilating them with all-sky infrared IR brightness temperatures (BTs). This study examines the impacts of assimilating combinations of GOES-16 all-sky IR brightness temperatures, NOAA P-3 TDR radial velocities, and conventional observations from the Global Telecommunications System (GTS) on the analyses and forecasts of Hurricane Dorian (2019). It is shown that including IR and/or TDR observations on top of conventional GTS observations significantly reduces both track and intensity forecast errors. Track errors are reduced the most (25% at lead times greater than 48 h) when TDR and GTS observations are assimilated. In terms of intensity, errors are always lower at lead times greater than 48 h when IR BTs are assimilated. Simultaneously assimilating TDR and IR observations has the potential to further improve the intensity forecast by as much as 37% at a lead time of 48 h to 72 h. The improved intensity forecasts produced by the experiments assimilating all three observation sources are shown to be a result of the competing effects of IR assimilation producing an overly broad area of strong cyclonic circulation and TDR assimilation constraining that circulation to a more realistic size and intensity. Interestingly, the order in which observations are assimilated has non-negligible impacts on the analyses and forecasts of Dorian.