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In this study, we assimilated microwave (MW) all‐sky radiances from low‐Earth‐orbiting satellites and examined their impact on the analyses and forecasts of weather hazards associated with the 10 August 2020 Midwest derecho. Compared with the baseline that assimilated conventional surface and upper‐air observations and infrared (IR) all‐sky radiances from geostationary satellites, the addition of MW all‐sky radiances improved the analyzed and forecasted convection‐stratiform structures of the derecho. Results show that MW all‐sky radiances provided additional information, compared with IR radiances, on hydrometeors within the storm, leading to improved forecasts out to 2 hr with quantitatively more accurate surface gusts. This is the first study to assimilate MW all‐sky radiances for a severe weather event using a convection‐permitting numerical weather prediction model (our model resembles NOAA's High‐Resolution Rapid Refresh), and the results suggest promising avenues for improving severe weather forecasts worldwide in the future. Plain Language Summary: Satellite observations are the backbone of modern weather forecast operations, especially for severe weather monitoring and prediction. However, they are also severely underutilized by computer weather models. Many satellite observations impacted by clouds and precipitation are not used in these models, despite their ability to characterize important features of ongoing severe weather events. This study focuses on satellite observations at microwave (MW) frequencies that are impacted by clouds and precipitation. We explore their potential benefits by incorporating them into a computer weather model using data assimilation and examining their impact on severe weather forecasts. We utilize the 10 August 2020 Midwest derecho that produced extensive wind damage as a case study. Because these MW observations contain contributions from precipitating particles within the derecho, assimilating these observations yields better depictions of the derecho structure in the computer model. Consequently, more accurate forecasts of surface gusts are produced. The results of this study suggest a promising avenue for improving severe weather forecasts worldwide in the future, especially for regions that lack the resources and infrastructure to support high‐spatiotemporal‐resolution weather observations. Key Points: Microwave (MW) all‐sky radiances are impacted by hydrometeors within clouds, complementing cloud‐top dominated infrared all‐sky radiancesAssimilating MW all‐sky radiances leads to improved storm structure in the analyses of the 10 August 2020 Midwest derechoWith improved depiction of the derecho's structure, accurate gust forecasts are obtained 2 hr earlier [ABSTRACT FROM AUTHOR]

On 28 April 2019, hourly forecasts from the operational High-Resolution Rapid Refresh (HRRR) model consistently predicted an isolated supercell storm late in the day near Dodge City, Kansas, that subsequently was not observed. Two convection-allowing model (CAM) ensemble runs are created to explore the reasons for this forecast error and implications for severe weather forecasting. The 40-member CAM ensembles are run using the HRRR configuration of the WRF-ARW Model at 3-km horizontal grid spacing. The Gridpoint Statistical Interpolation (GSI)-based ensemble Kalman filter is used to assimilate observations every 15 min from 1500 to 1900 UTC, with resulting ensemble forecasts run out to 0000 UTC. One ensemble only assimilates conventional observations, and its forecasts strongly resemble the operational HRRR with all ensemble members predicting a supercell storm near Dodge City. In the second ensemble, conventional observations plus observations of WSR-88D radar clear-air radial velocities, WSR-88D diagnosed convective boundary layer height, and GOES-16 all-sky infrared brightness temperatures are assimilated to improve forecasts of the preconvective environment, and its forecasts have half of the members predicting supercells. Results further show that the magnitude of the low-level meridional water vapor flux in the moist tongue largely separates members with and without supercells, with water vapor flux differences of 12% leading to these different outcomes. Additional experiments that assimilate only radar or satellite observations show that both are important to predictions of the meridional water vapor flux. This analysis suggests that mesoscale environmental uncertainty remains a challenge that is difficult to overcome. Significance Statement: Forecasts from operational numerical models are the foundation of weather forecasting. There are times when these models make forecasts that do not come true, such as 28 April 2019 when successive forecasts from the operational High-Resolution Rapid Refresh (HRRR) model predicted a supercell storm late in the day near Dodge City, Kansas, that subsequently was not observed. Reasons for this forecast error are explored using numerical experiments. Results suggest that relatively small changes to the prestorm environment led to large differences in the evolution of storms on this day. This result emphasizes the challenges to operational severe weather forecasting and the continued need for improved use of all available observations to better define the atmospheric state given to forecast models. [ABSTRACT FROM AUTHOR]

Accurate predictions of the location and timing of convection initiation (CI) remain a challenge, even in high-resolution convection-allowing models (CAMs). Many of the processes necessary for daytime CI are rooted in the planetary boundary layer (PBL), which numerical models struggle to accurately predict. To improve ensemble forecasts of the PBL and subsequent CI forecasts in CAM ensembles, we explore the use of underused data from both the GOES-16 satellite and the national network of WSR-88D radars. The GOES-16 satellite provides observations of brightness temperature (BT) to better analyze cloud structures, while the WSR-88D radars provide PBL height estimates and clear-air radial wind velocity observations to better analyze PBL structures. The CAM uses the Advanced Research Weather Research and Forecasting (WRF-ARW) Model at 3-km horizontal grid spacing. The ensemble consists of 40 members and observations are assimilated using the Gridpoint Statistical Interpolation (GSI) ensemble Kalman filter (EnKF) system. To evaluate the influence of each observation type on CI, conventional, WSR-88D, and GOES-16 observations are assimilated separately and jointly over a 4-h period and the resulting ensemble analyses and forecasts are compared with available observations for a CI event on 18 May 2018. Results show that the addition of the WSR-88D and GOES-16 observations improves the CI forecasts out several hours in terms of timing and location for this case. Significance Statement: The location and timing of new thunderstorm development is an important component of severe weather forecasts. Yet the prediction of thunderstorm development in weather prediction models remains challenging. This study explores using a combination of underused satellite and radar observations to better define the atmospheric state used to start the weather prediction models, with the hope that this will lead to better forecasts of new thunderstorm development. Results show that underused observations from routinely available Doppler weather radars and a geostationary satellite, all of which are currently available, can work synergistically to improve forecasts of the location and timing of severe thunderstorm development. [ABSTRACT FROM AUTHOR]

Horizontal convective rolls (HCRs) with aspect ratios ≥ 5, called wide HCRs, are observed over land from WSR-88D radar reflectivity observations in clear air over central Oklahoma. Results indicate that wide HCRs are a natural part of the daily HCR life cycle, occurring most frequently from 1500 to 1700 UTC and from 2300 to 2400 UTC, with the HCRs having aspect ratios ∼ 3 during the rest of their lifetime. Wide HCRs are most likely to be observed from HCRs with lifetimes longer than 5 h. Results show that for HCRs lasting for more than 5 h, 12% have aspect ratios ≥ 5 during HCR formation, whereas 50% of have aspect ratios ≥ 5 at dissipation. An evaluation of radar observations from 50 cases of long-lived HCRs suggests the wide HCRs that occur in tandem with HCR formation early in the day develop in situ with a large aspect ratio. In contrast, the cases of wide HCRs that form late in the day most often appear to develop as specific HCR wavelengths are maintained while roll circulations with smaller wavelengths dissipate. These ephemeral wide HCRs over land deserve attention as the mechanisms leading to their formation are unclear. Significance Statement: The atmospheric boundary layer extends from the ground up to a typical daytime height between 500 m and 3 km. Within this layer, the flow is often turbulent during the daytime, although there are common structures that help to organize the flow patterns. One of these structures is a field of horizontal counterrotating helical circulations, with parallel upwelling and downwelling zones. This study shows that the separation distance between these long parallel lines of upward and downward motion changes during the day and can be quite large when compared to the depth of the boundary layer, both early in the day and late in the day. Reasons for this behavior are unclear and deserve attention, as the boundary layer is where we spend our lives and has a large influence on our daily activities. [ABSTRACT FROM AUTHOR]

Typical environmental conditions associated with horizontal convective rolls (HCRs) and cellular convection have been known for over 50 years. Yet our ability to predict whether HCRs, cellular convection, or no discernable organized (null) circulation will occur within a well-mixed convective boundary layer based upon easily observed environmental variables has been limited. Herein, a large database of 50 cases each of HCR, cellular convection, and null events is created that includes observations of mean boundary layer wind and wind shear, boundary layer depth; surface observations of wind, temperature, and relative humidity; and estimates of surface sensible heat flux. Results from a multiclass linear discriminant analysis applied to these data indicate that environmental conditions can be useful in predicting whether HCRs, cellular convection, or no circulation occurs, with the analysis identifying the correct circulation type on 72% of the case days. This result is slightly better than using a mean convective boundary layer (CBL) wind speed of 6ms21 to discriminate between HCRs and cells. However, the mean CBL wind speed has no ability to further separate out cases with no CBL circulation. The key environmental variables suggested by the discriminant analysis are mean sensible heat flux, friction velocity, and the Obukhov length. [ABSTRACT FROM AUTHOR]

Recent studies have demonstrated advances in the analysis and prediction of severe thunderstorms and other weather hazards by assimilating infrared (IR) all-sky radiances into numerical weather prediction models using advanced ensemble-based techniques. It remains an open question how many of these advances are due to improvements in the radiance observations themselves, especially when compared with radiance observations from preceding satellite imagers. This study investigates the improvements gained by assimilation of IR all-sky radiances from the Advanced Baseline Imager (ABI) on board GOES-16 compared to those from its predecessor imager. Results show that all aspects of the improvements in ABI compared with its predecessor imager—finer spatial resolution, shorter scanning intervals, and more channels covering a wider range of the spectrum—contribute to more accurate ensemble analyses and forecasts of the targeted severe thunderstorm event, but in different ways. The clear-sky regions within the assimilated all-sky radiance fields have a particularly beneficial influence on the moisture fields. Results also show that assimilating different IR channels can lead to oppositely signed increments in the moisture fields, a by-product of inaccurate covariances at large distances resulting from sampling errors. These findings pose both challenges and opportunities in identifying appropriate vertical localizations and IR channel combinations to produce the best possible analyses in support of severe weather forecasting. Significance Statement: Using the PSU WRF-EnKF system, we find that all of the improvements (i.e., finer spatial resolution, shorter scanning intervals, and more channels that cover a wider spectral range, and especially a channel that is sensitive to lower-tropospheric moisture) associated with the Advanced Baseline Imager (ABI) on board GOES-16 compared with its predecessor imager on board GOES-13 contribute to the improvement of ensemble-based analysis and forecast of severe thunderstorms during an event in the Great Plains via all-sky radiance assimilation. The results not only provide information on how to assimilate satellite infrared observations more efficiently and effectively for severe weather prediction, but also provide a starting point to explore the spatial resolution, temporal resolution, and spectral coverage for which future spaceborne Earth-observing systems should aim. [ABSTRACT FROM AUTHOR]

Nocturnal convection is often initiated by mechanisms that cannot be easily observed within the large gaps between rawinsondes or by conventional surface networks. To improve forecasts of such events, we evaluate the systematic impact of assimilating a collocated network of high-frequency, ground-based thermodynamic and kinematic profilers collected as part of the 2015 Plains Elevated Convection At Night (PECAN) experiment. For 13 nocturnal convection initiation (CI) events, we find small but consistent improvements when assimilating thermodynamic observations collected by Atmospheric Emitted Radiance Interferometers (AERIs). Through midlevel cooling and moistening, assimilating the AERIs increases the fractions skill score (FSS) for both nocturnal CI and precipitation forecasts. The AERIs also improve various contingency metrics for CI forecasts. Assimilating composite kinematic datasets collected by Doppler lidars and radar wind profilers (RWPs) results in slight degradations to the forecast quality, including decreases in the FSS and traditional contingency metrics. The impacts from assimilating thermodynamic and kinematic profilers often counteract each other, such that we find little impact on the detection of CI when both are assimilated. However, assimilating both datasets improves various properties of the CI events that are successfully detected (timing, distance, shape, etc.). We also find large variability in the impact of assimilating these remote sensing profilers, likely due to the number of observing sites and the strength of the synoptic forcing for each case. We hypothesize that the lack of flow-dependent methods to diagnose observation errors likely contributes to degradations in forecast skill for many cases, especially when assimilating kinematic profilers. [ABSTRACT FROM AUTHOR]

The Orinoco low‐level jet (OLLJ) is characterized using finer horizontal, vertical, and temporal resolution than possible in previous studies via dynamical downscaling. The investigation relies on a 5‐month‐long simulation (November 2013 to March 2014) performed with the Weather Research and Forecasting (WRF) model, with initial and boundary conditions provided by the Global Forecast System (GFS) analysis. Dynamical downscaling is demonstrated to be an effective method not only to better resolve the horizontal and vertical characteristics of the OLLJ but also to determine the mechanisms leading to its formation. The OLLJ is a single stream tube over Colombia and Venezuela with wind speeds greater than 8 m s−1 and four distinctive cores of higher wind speeds varying in height under the influence of sloping terrain. It is an austral summer phenomenon that exhibits its seasonal maximum wind speed and largest spatial extent (2,100 km × 450 km) in January. The maximum diurnal mean wind speeds (13–17 m s−1) at each core location occur at different times during the night and early morning (2300–0900 LST). The momentum balance analysis in a natural coordinate system reveals that the OLLJ results from four phenomena acting together to accelerate the wind: a sea breeze penetration, katabatic flow, three expansion fans, and diurnal variation of turbulent diffusivity. The latter, in contrast to the heavily studied nocturnal low‐level jet in the U.S. Great Plains region, plays a secondary role in OLLJ acceleration. These results imply that LLJs near the equator may originate from processes other than the inertial oscillation and topographic thermal forcing. Key Points: The Orinoco low‐level jet is the result of four phenomena acting together to accelerate the wind over the valley of the Orinoco River basinOpposite to what happens in higher latitudes, the role of the diurnal variation of turbulent diffusivity in the Orinoco low‐level jet acceleration is secondaryLow‐level jets near the equator may originate from processes other than the inertial oscillation and topographic thermal forcing [ABSTRACT FROM AUTHOR]

Horizontal convective rolls (HCRs) and cellular convection (cells) are frequently observed within the planetary boundary layer. Yet understanding of the evolution, seasonal variation, and characteristics of such boundary layer phenomena is limited as previous studies used observations from field experiments or satellites. As a result, little is known about the mean climatology and monthly variation of HCRs and cells. Polarimetric WSR-88D radar observations are used to develop a 10-yr April-September climatology in central Oklahoma including HCR and cell occurrence, duration, and aspect ratios as well as HCR orientation angles and wavelengths. Results indicate that HCRs or cells occur on over 92% of days without precipitation during the warm season. HCRs or cells typically form in midmorning and may persist throughout the day or transition between modes before dissipating around sunset. HCRs generally persist for 1-6 h with typical wavelengths of 2-10 km and most aspect ratios between 1 and 7. Rolls are often oriented within 108 of the mean boundary layer wind but can be as much as 308 off this direction. Mean HCR aspect ratios in this study remain constant during the afternoon, but decrease early in the day and increase late in the day, diverging from previous overland HCR studies. Cells generally persist for 2-6 h with aspect ratios of 1-6. These results should facilitate future studies on convection initiation, formation mechanisms of boundary layer organization, and model parameterization. [ABSTRACT FROM AUTHOR]

We report for the first time on a distributed acoustic sensing (DAS) array using preexisting underground fiber optics beneath the Penn State campus for detecting and characterizing thunder‐induced ground motions. During a half‐hour interval from 03:20–03:50 UTC on 15 April 2019 in State College, PA, we identify 18 thunder‐induced seismic events in the DAS array data. The high‐fidelity DAS data show that the thunder‐induced seismics are very broadband, with their peak frequency ranging from 20 to 130 Hz. We use arrival times of the 18 events to estimate the phase velocity of the near surface, the back azimuth, and location of thunder‐seismic sources that are verified with lightning locations from the National Lightning Detection Network. Furthermore, the dense DAS data enable us to simulate thunder‐seismic wave propagation and full waveform synthetics and further locate the thunder‐seismic source by time‐reversal migration. Interestingly, we found that thunder‐seismic power recorded by DAS is positively correlated with National Lightning Detection Network lightning current power. These findings suggest that fiber‐optic DAS observations may offer a new avenue of studying thunder‐induced seismics, characterizing the near‐surface velocity structure, and probing the thunder‐ground coupling process. Plain Language Summary: Detailed understanding of how weather processes in the atmosphere couple into seismic waves in the solid Earth is extremely important and will require a high‐resolution observation of this coupling in the temporal and spatial scales. This study is the first to demonstrate the use of preexisting telecommunication fiber optics beneath the Penn State campus to characterize thunder‐induced seismic waves in the near surface. The fiber‐optic array is composed of up to tens of kilometers of fiber‐optic cable and can achieve the high resolution at the scale of meters. The high‐fidelity distributed acoustic sensing (DAS) data can provide a detailed characterization of thunder‐induced signals and further track thunder‐induced sources. Moreover, this is the first study to demonstrate the relationship between thunder current energy and DAS recorded ground motion power, suggesting the DAS observations can be useful for characterizing thunder‐ground motions and probing the complex thunder‐ground coupling process. Key Points: The first observations of thunder‐induced seismic waves are by the distributed acoustic sensing (DAS) array with preexisting telecommunication fiber opticsDense DAS data reveal the characteristics of thunder‐seismic signals and enable seismic full waveform analysis to conductA positive correlation between DAS seismic wave power and lightning current power was found [ABSTRACT FROM AUTHOR]

This study explores the benefits of assimilating infrared (IR) brightness temperature (BT) observations from geostationary satellites jointly with radial velocity (Vr) and reflectivity (Z) observations from Doppler weather radars within an ensemble Kalman filter (EnKF) data assimilation system to the convection-allowing ensemble analysis and prediction of a tornadic supercell thunderstorm event on 12 June 2017 across Wyoming and Nebraska. While radar observations sample the three-dimensional storm structures with high fidelity, BT observations provide information about clouds prior to the formation of precipitation particles when instorm radar observations are not yet available and also provide information on the environment outside the thunderstorms. To better understand the strengths and limitations of each observation type, the satellite and Doppler radar observations are assimilated separately and jointly, and the ensemble analyses and forecasts are compared with available observations. Results show that assimilating BT observations has the potential to increase the forecast and warning lead times of severe weather events compared with radar observations and may also potentially complement the sparse surface observations in some regions as revealed by the probabilistic prediction of mesocyclone tracks initialized from EnKF analyses as various times. Additionally, the assimilation of both BT and Vr observations yields the best ensemble forecasts, providing higher confidence, improved accuracy, and longer lead times on the probabilistic prediction of midlevel mesocyclones. [ABSTRACT FROM AUTHOR]

The structure and evolution of the low‐level jet over the Orinoco River basin is characterized using finer horizontal, vertical, and temporal resolution than possible in previous studies via dynamical downscaling. The investigation relies on a 5‐month‐long simulation (November 2013 to March 2014) performed with the Weather Research and Forecasting model, with initial and boundary conditions provided by the Global Forecast System analysis. Dynamical downscaling is demonstrated to be an effective method to better resolve the horizontal and vertical characteristics of the Orinoco low‐level jet (OLLJ), improving not only the representation of small‐scale jet streaks within the broader region of low‐level wind enhancement but also its diurnal and austral‐summer evolution. The OLLJ is found to be a single stream tube over Colombia and Venezuela with wind speeds greater than 8 m/s and four distinctive cores varying in height under the influence of sloping terrain. The OLLJ has its maximum monthly mean wind speed (13 m/s) and largest spatial extent (2,100 km × 400 km) in January. The maximum mean wind speeds (13–17 m/s) in the diurnal cycle occur in the early morning, whereas wind speeds are a minimum (8–9 m/s) in the late afternoon when a deep, convective boundary layer is present. The intraseasonal variability of the wind speed and potential temperature only presents significant periodicity in the diurnal and semidiurnal scales, with no other meaningful cycles evident during the austral summer. Key Points: The Orinoco low‐level jet (300 km wide to 2.5 km deep approximately) is present from November to March and can extend over 2,000 km from the Venezuelan coast to central ColombiaIt is weakest (peak wind speeds ~9 m/s) in November and strongest (peak wind speed ~13 m/s) in JanuaryIt is generally most intense (mean wind speeds ~12–15 m/s) in the morning hours (~0700 LST) and least intense (mean wind speeds ~9 m/s) in the afternoon (~1500 LST) [ABSTRACT FROM AUTHOR]

Numerical weather prediction models often fail to correctly forecast convection initiation (CI) at night. To improve our understanding of such events, researchers collected a unique dataset of thermodynamic and kinematic remote sensing profilers as part of the Plains Elevated Convection at Night (PECAN) experiment. This study evaluates the impacts made to a nocturnal CI forecast on 26 June 2015 by assimilating a network of atmospheric emitted radiance interferometers (AERIs), Doppler lidars, radio wind profilers, high-frequency rawinsondes, and mobile surface observations using an advanced, ensemble-based data assimilation system. Relative to operational forecasts, assimilating the PECAN dataset improves the timing, location, and orientation of the CI event. Specifically, radio wind profilers and rawinsondes are shown to be the most impactful instrument by enhancing the moisture advection into the region of CI in the forecast. Assimilating thermodynamic profiles collected by the AERIs increases midlevel moisture and improves the ensemble probability of CI in the forecast. The impacts of assimilating the radio wind profilers, AERI retrievals, and rawinsondes remain large throughout forecasting the growth of the CI event into a mesoscale convective system. Assimilating Doppler lidar and surface data only slightly improves the CI forecast by enhancing the convergence along an outflow boundary that partially forces the nocturnal CI event. Our findings suggest that a mesoscale network of profiling and surface instruments has the potential to greatly improve short-term forecasts of nocturnal convection. [ABSTRACT FROM AUTHOR]

In support of the Next Generation Global Prediction System (NGGPS) project, processes leading to convection initiation in the North American Mesoscale Forecast System, version 3 (NAMv3) are explored. Two severe weather outbreaks—occurring over the southeastern United States on 28 April 2014 and the central Great Plains on 6 May 2015—are forecast retrospectively using the NAMv3 CONUS (4 km) and Fire Weather (1.33 km) nests, each with 5-min output. Points of convection initiation are identified, and patterns leading to convection initiation in the model forecasts are determined. Results indicate that in the 30 min preceding convection initiation at a grid point, upward motion at low levels of the atmosphere enables a parcel to rise to its level of free convection, above which it is accelerated by the buoyancy force. A moist absolutely unstable layer (MAUL) typically is produced at the top of the updraft. However, when strong updrafts are collocated with large vertical gradients of potential temperature and moisture, noisy vertical profiles of temperature, moisture, and hydrometeor concentration develop beneath the rising MAUL. The noisy profiles found in this study are qualitatively similar to those that resulted in NAMv3 failures during simulations of Hurricane Joaquin in 2015. The CM1 cloud model is used to reproduce these noisy profiles, and results indicate that the noise can be mitigated by including explicit vertical diffusion in the model. Left unchecked, the noisy profiles are shown to impact convective storm features such as cold pools, precipitation, updraft helicity intensity and tracks, and the initiation of spurious convection. [ABSTRACT FROM AUTHOR]

Convection intensity and longevity is highly dependent on the surrounding environment. Ensemble sensitivity analysis (ESA), which quantitatively and qualitatively interprets impacts of initial conditions on forecasts, is applied to very short-term (1–2 h) convective-scale forecasts for three cases during the Mesoscale Predictability Experiment (MPEX) in 2013. The ESA technique reveals several dependencies of individual convective storm evolution on their nearby environments. The three MPEX cases are simulated using a previously verified 36-member convection-allowing model (Δx = 3 km) ensemble created via the Weather Research and Forecasting (WRF) Model. Radar and other conventional observations are assimilated using an ensemble adjustment Kalman filter. The three cases include a mesoscale convective system (MCS) and both nontornadic and tornadic supercells. Of the many ESAs applied in this study, one of the most notable is the positive sensitivity of supercell updraft helicity to increases in both storm inflow region deep and shallow vertical wind shear. This result suggests that larger values of vertical wind shear within the storm inflow yield higher values of storm updraft helicity. Results further show that the supercell storms quickly enhance the environmental vertical wind shear within the storm inflow region. Application of ESA shows that these storm-induced perturbations then affect further storm evolution, suggesting the presence of storm–environment feedback cycles where perturbations affect future mesocyclone strength. Overall, ESA can provide insight into convection dependencies on the near-storm environment. [ABSTRACT FROM AUTHOR]

This article presents the first practice of assimilating real-world all-sky GOES-16 ABI infrared brightness temperature (BT) observations using an ensemble-based data assimilation system coupled with the Weather Research and Forecasting (WRF) Model at a convection-allowing (1 km) horizontal resolution, focusing on the tornadic thunderstorm event across Wyoming and Nebraska on 12 June 2017. It is found that spurious clouds created before observed convection initiation are rapidly removed, and the analysis and forecasts of thunderstorms are significantly improved, when all-sky BT observations are assimilated with the adaptive observation error inflation (AOEI) and adaptive background error inflation (ABEI) techniques. Better forecasts of the timing and location of convection initiation can be achieved after only 30 min of assimilating BT observations; both deterministic and probabilistic WRF forecasts of midlevel mesocyclones and low-level vortices, started from the final analysis with 100 min of BT assimilation, closely coincide with the tornado reports. These improvements result not only from the effective suppression of spurious clouds, but also from the better estimations of hydrometeors owing to the frequent assimilation of all-sky BT observations that yield a more accurate analysis of the storm. Results show that BT observations generally have a greater impact on ice particles than liquid water species, which might provide guidance on how to better constrain modeled clouds using these spaceborne observations. [ABSTRACT FROM AUTHOR]

This study investigates Bragg scatter signatures in dual-polarization radar observations, which are defined by low differential reflectivity (ZDR) values, as a proxy for convective boundary layer (CBL) depth. Using data from the WSR-88D in Twin Lakes, Oklahoma (KTLX), local minima in quasi-vertical profiles of ZDR are found to provide a reasonable estimate of CBL depth when compared with depth estimates from upper-air soundings from Norman, Oklahoma (KOUN), during 2014. The 243 ZDR Bragg scatter and upper-air sounding CBL depth estimates have a correlation of 0.90 and an RMSE of 254 m. Using Bragg scatter as a proxy for CBL depth was expanded to other seasons and locations--performing well in Wilmington, Ohio; Fairbanks, Alaska; Tucson, Arizona; Minneapolis, Minnesota; Albany, New York; Portland, Oregon; and Tampa, Florida--showing its potential usefulness in monitoring CBL depth throughout the year in a variety of geographic locations and meteorological conditions. [ABSTRACT FROM AUTHOR]

The initiation of new convection at night in the Great Plains contributes to a nocturnal maximum in precipitation and produces localized heavy rainfall and severe weather hazards in the region. Although previous work has evaluated numerical model forecasts and data assimilation (DA) impacts for convection initiation (CI), most previous studies focused only on convection that initiates during the afternoon and not explicitly on nocturnal thunderstorms. In this study, we investigate the impact of assimilating in situ and radar observations for a nocturnal CI event on 25 June 2013 using an ensemble-based DA and forecast system. Results in this study show that a successful CI forecast resulted only when assimilating conventional in situ observations on the inner, convection-allowing domain. Assimilating in situ observations strengthened preexisting convection in southwestern Kansas by enhancing buoyancy and locally strengthening low-level convergence. The enhanced convection produced a cold pool that, together with increased convergence along the northwestern low-level jet (LLJ) terminus near the region of CI, was an important mechanism for lifting parcels to their level of free convection. Gravity waves were also produced atop the cold pool that provided further elevated ascent. Assimilating radar observations further improved the forecast by suppressing spurious convection and reducing the number of ensemble members that produced CI along a spurious outflow boundary. The fact that the successful CI forecasts resulted only when the in situ observations were assimilated suggests that accurately capturing the preconvective environment and specific mesoscale features is especially important for nocturnal CI forecasts. [ABSTRACT FROM AUTHOR]

The effect of urban areas on weakly forced precipitation systems has been studied extensively. However, interactions between urban areas and strongly forced convection, such as supercells, remain relatively unexamined. The present study uses simulations of a supercell to quantify the impacts of a large plains urban area on the evolution and strength of a supercell. An initial ensemble of simulations (CTRLE) of a supercell over homogeneous land use is performed using the WRF-ARW Model. Additionally, 108 simulations are conducted in which the land-use pattern of Dallas-Ft. Worth, Texas, is placed inside the model domain, with the city center shifted to be in or near the path of the supercell. Simulations with urban areas are compared to CTRLE, with the aid of hierarchical clustering analysis to form statistically similar groups of simulations. Clustering analyses identify groups of ensemble members with closely located urban areas that have similar patterns of 0-1-km updraft helicity and near-surface minimum temperature and maximum wind speeds. Comparison of these groups of ensemble members to CTRLE suggests the urban area has a significant impact on storm characteristics, particularly on low-level rotation and mesocyclone track. Simulations where the storm passes to the north of or directly over the city center late in its life cycle deviate most significantly from CTRLE. In these members, low-and midlevel mesocyclone strength increases, and the mesocyclone tracks farther south when compared to CTRLE. [ABSTRACT FROM AUTHOR]

In this study, the ensemble of three-dimensional variational data assimilation (En3DVar) method for convective-scale weather is adopted and evaluated using an idealized supercell storm simulated by the Weather Research and Forecasting (WRF) Model. Synthetic radar radial velocity, reflectivity, satellitederived cloud water path (CWP), and total precipitable water (TPW) data are produced from the simulated supercell storm and then these data are assimilated into another WRF Model run that starts with no convection. Two types of experiments are performed. The first assimilates radar and satellite CWP data using a perfect storm environment. The second assimilates additional TPW data using a storm environment with dry bias. The first set of experiments indicates that incorporating CWP and radar data into the assimilation leads to a much faster initiation of supercell storms than found using radar data alone. Assimilating CWP data primarily improves the analyses of nonprecipitating hydrometeor variables. The results from the second set of experiments demonstrate the critical importance of the storm environment. When using the biased storm environment, assimilation of CWP and radar data enhances the analyses, but the forecast skill rapidly decreases over the subsequent 1-h forecast. Further experiments show that assimilating theTPWdata has a large impact on storm environment that is essential to the accuracy of the storm forecasts. In general, the combination of radar data and satellite data within the En3DVar results in better analyses and forecasts than when only radar data are used, especially for an imperfect storm environment. [ABSTRACT FROM AUTHOR]

The Mesoscale Predictability Experiment (MPEX) conducted during the spring of 2013 included frequent coordinated sampling of near-storm environments via upsondes. These unique observations were taken to better understand the upscale effects of deep convection on the environment, and are used to validate the accuracy of convection-allowing (Δx 5 3 km) model ensemble analyses. A 36-member ensemble was created with physics diversity using the Weather Research and Forecasting Model, and observations were assimilated via the Data Assimilation Research Testbed using an ensemble adjustment Kalman filter. A 4-day sequence of convective events from 28 to 31 May 2013 in the south-central United States was analyzed by assimilating Doppler radar and conventional observations. No MPEX upsonde observations were assimilated. Since the ensemble mean analyses produce an accurate depiction of the storms, the MPEX observations are used to verify the accuracy of the analyses of the near-storm environment. A total of 81 upsondes were released over the 4-day period, sampling different regions of near-storm environments including storm inflow, outflow, and anvil. The MPEX observations reveal modest analysis errors overall when considering all samples, although specific environmental regions reveal larger errors in some state fields. The ensemble analyses underestimate cold pool depth, and storm inflow meridional winds have a pronounced northerly bias that results from an underprediction of inflow wind speed magnitude. Most bias distributions are Gaussian-like, with a few being bimodal owing to systematic biases of certain state fields and environmental regions. [ABSTRACT FROM AUTHOR]

This study uses an observing system simulation experiment to explore the impact of assimilating GOES-R Advanced Baseline Imager (ABI) 6.95- μm brightness temperatures and Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity observations in an ensemble data assimilation system. A high-resolution truth simulation was used to create synthetic radar and satellite observations of a severe weather event that occurred across the U.S. central plains on 4-5 June 2005. The experiment employs the Weather Research and Forecasting Model at 4-km horizontal grid spacing and the ensemble adjustment Kalman filter algorithm in the Data Assimilation Research Testbed system. The ability of GOES-R ABI brightness temperatures to improve the analysis and forecast accuracy when assimilated separately or simultaneously with Doppler radar reflectivity and radial velocity observations was assessed, along with the use of bias correction and different covariance localization radii for the brightness temperatures. Results show that the radar observations accurately capture the structure of a portion of the storm complex by the end of the assimilation period, but that more of the storms and atmospheric features are reproduced and the accuracy of the ensuing forecast improved when the brightness temperatures are also assimilated. [ABSTRACT FROM AUTHOR]

An ensemble of the three-dimensional variational data assimilation (En3DA) method for convective-scale weather has been developed. It consists of an ensemble of three-dimensional variational data assimilations and forecasts in which member differences are introduced by perturbing initial conditions and/or observations, and it uses flow-dependent error covariances generated by the ensemble forecasts. The method is applied to the assimilation of simulated radar data for a supercell storm. Results indicate that the flow-dependent ensemble covariances are effective in enabling convective-scale analyses, as the most important features of the simulated storm, including the low-level cold pool and midlevel mesocyclone, are well analyzed. Several groups of sensitivity experiments are conducted to test the robustness of the method. The first group demonstrates that incorporating a mass continuity equation as a weak constraint into the En3DA algorithm can improve the quality of the analyses when radial velocity observations contain large errors. In the second group of experiments, the sensitivity of analyses to the microphysical parameterization scheme is explored. Results indicate that the En3DA analyses are quite sensitive to differences in the microphysics scheme, suggesting that ensemble forecasts with multiple microphysics schemes could reduce uncertainty related to model physics errors. Experiments also show that assimilating reflectivity observations can reduce spinup time and that it has a small positive impact on the quality of the wind field analysis. Of the threshold values tested for assimilating reflectivity observations, 15 dB Z provides the best analysis. The final group of experiments demonstrates that it is not necessary to perturb radial velocity observations for every ensemble number in order to improve the quality of the analysis. [ABSTRACT FROM AUTHOR]