Storm Structure Research Articles

Consistent Impacts of Surface Enthalpy and Drag Coefficient Uncertainty between an Analytical Model and Simulated Tropical Cyclone Maximum Intensity and Storm Structure

Abstract Several previous studies have demonstrated the significant sensitivity of simulated tropical cyclone structure and intensity to variations in surface-exchange coefficients for enthalpy (Ck) and momentum (Cd), respectively. In this study we investigate the consistency of the estimated peak intensity, intensification rate, and steady-state structure between an analytical model and idealized axisymmetric numerical simulations for both constant Ck and Cd values and various wind speed–dependent representations of Ck and Cd. The present analysis with constant Ck and Cd values demonstrates that the maximum wind speed is similar for identical Ck/Cd values less than 1, regardless of whether changes were made to Ck or Cd. However, for a given Ck/Cd greater than 1, the simulated and theoretical maximum wind speed are both greater if Cd is decreased compared to Ck increased. This behavior results because of a smaller enthalpy disequilibrium at the radius of maximum winds for larger Ck. Additionally, the intensification rate is shown to increase with Ck and Cd and the steady-state normalized wind speed beyond the radius of maximum winds is shown to increase with increasing Cd. Experiments with wind speed–dependent Ck and Cd were found to be generally consistent, in terms of the intensification rate and the simulated and analytical-model-estimated maximum wind speed, with the experiments with constant Ck and Cd.

Impact of Assimilating Future Clear-Air Radial Velocity Observations from Phased-Array Radar on a Supercell Thunderstorm Forecast: An Observing System Simulation Experiment Study

Abstract Phased-array radar (PAR) technology offers the flexibility of sampling the storm and clear-air regions with different update times. As such, the radial velocity from clear-air regions, typically with a lower signal-to-noise ratio, can be measured more accurately. In this work, observing system simulation experiments are conducted to explore the potential value of assimilating clear-air radial velocity observations to improve numerical prediction of supercell thunderstorms. Synthetic PAR observations of a splitting supercell are assimilated at different life cycle stages using an ensemble Kalman filter. Results show that assimilating environmental clear-air radial velocity can reduce wind errors in the near-storm environment and within the precipitation region. Improvements in the forecast are seen at different stages, especially for the forecast after 30 min. After assimilating clear-air radial velocity observations, the probabilities of updraft helicity and precipitation within the corresponding swaths of the truth simulation increase up to 30%–40%. Additional diagnostics suggest that the more accurate track forecast, stronger vertical motion, and better-maintained supercell can be attributed to the better analysis and prediction of the mean environmental winds and linear and nonlinear dynamic forces. Consequently, assimilating clear-air radial velocity produces accurate storm structure (rotating updrafts), updraft size, and storm track, and improves the surface accumulated precipitation forecast. The performance of forecasts with a higher frequency of assimilating clear-air radial velocity does not show systematic improvement. These results highlight the potential of assimilating clear-air radial velocity observations to improve numerical weather prediction forecasts of supercell thunderstorms.

Upper‐tropospheric inflow layers in tropical cyclones

AbstractThree‐dimensional numerical simulations of tropical cyclone intensification with sufficient vertical resolution have shown the development of a layer of strong inflow just beneath the upper‐tropospheric outflow layer as well as, in some cases, a shallower layer of weaker inflow above the outflow layer. Here we provide an explanation for these inflow layers in the context of the prototype problem for tropical cyclone intensification, which considers the evolution of a vortex on an f‐plane in a quiescent environment, starting from an initially symmetric, moist, cloud‐free vortex over a warm ocean. We attribute the inflow layers to a subgradient radial force that exists through much of the upper troposphere beyond a certain radius. An alternative explanation that invokes classical axisymmetric balance theory is found to be problematic. We review evidence for the existence of such inflow layers in recent observations. Some effects of the inflow layers on the storm structure are discussed.

Electrical evolution of a rapidly developing MCS during its vigorous vertical growth phase

This study examined the coevolution of storm structure and electrical behavior of a rapidly developing mesoscale convective system (MCS) over Beijing metropolitan region during its vigorous growth phase based on observations and model simulations. When the isolated convection grew upscale and developed into a larger and organized MCS, it is found that the lightning production increased much faster than storm volume growth. Although the overall MCS went through rapid intensification, large differences exist in the radar reflectivity pattern of its convective cell members indicating their differential convective states. Several distribution peaks of lightning radiation pulses are also observed in the vertical direction, suggesting the overall complicated charge distribution inside the MCS. Given the decreased distances among cells during the upscale growth, it is hypothesized that such complicated charge distributions are more conducive to local high electric fields for lightning initiations and hence increased lightning production rates. The electrification-coupled WRF simulations support this proposed hypothesis, which clearly show that the MCS evolves from two layered charge regions to more complicated charge distributions. When some convective cells consisting of the MCS develop toward maturity, the precipitation particles descend to low levels and give rise to a lower positive charge region that is embedded inside a deep-layer negative charge region. In combination with enhanced interactions among the cells, a peak of electric field magnitude is generated at lower levels due to the approaching charge regions with opposite polarities, such that more lightning flashes are produced.

Significance of 4DVAR Radar Data Assimilation in Weather Research and Forecast Model‐Based Nowcasting System

AbstractAccurate nowcasting of short‐lived extreme weather events is essential for saving millions of lives and property. Traditional methods of nowcasting are majorly focused on extrapolation of precipitation derived from radar reflectivity data, which often fail to capture the initiation and decay of weather systems. Earlier studies have shown the ability of high‐resolution Numerical Weather Prediction (NWP) models to better capture the structure and lifecycle of storms compared to data‐driven methods. However, the initial value problem of NWP makes it more challenging to be implemented for nowcasting applications. To handle such uncertainty from initial conditions, we have designed an NWP nowcasting system based on variational approach using WRF model. One of the major challenges of the variational methods in the nowcasting system is the choice of control variables used for generating background error statistics. Thus, we have investigated the impact of control variable options on improving the skill of variational‐based NWP nowcasting system. The proposed nowcasting system was tested for a heavy rainfall event that occurred over the Chennai city, India, on 1 December 2015, by assimilating Doppler Weather Radar data using different control variable options in Weather Research and Forecast—three‐dimensional (3DVAR)‐ and four‐dimensional variational data assimilation (4DVAR)‐based nowcasting system. Results show that control variables choices have a positive impact on 4DVAR analysis, particularly on radial velocity. Our results also indicate that assimilation of Doppler Weather Radar data with zonal and meridional momentum control variable in a 4DVAR system shows more than 30% improvement in precipitation forecast skill compared to the 3DVAR system.

An improved hydrometeor identification method for X-band dual-polarization radar and its application for one summer Hailstorm over Northern China

Currently, high-resolution severe storm observations obtained by vehicle-mounted mobile X-band dual-polarization radar (X-pol) are widely used in severe storm structure and dynamics studies; however, the identification of hailstones based on X-pol is still rare, leading to the limited application of X-pol in identifying hydrometeors in the microphysical processes of severe storms. Based on previous works, this study constructs an improved X-pol hydrometeor identification (HID) method for hailstorms; the hydrometeor categories include a rain-hail mixtures (RH) category indicating hailstones are melting or experiencing wet growth, and the correlation coefficient between horizontally and vertically polarized echoes (ρHV) is used to locate the local mixed phase region in storms to avoid misclassifications of hydrometeors resulted by environmental temperature in this local mixed phase region. The improved HID method can see liquid water information above the melting layer; it provides a possibility to observe the microphysical process of hailstones wet growth above the melting layer. Furthermore, this improved HID method was utilized to study the high spatiotemporal resolution data collected by the Institute of Atmospheric Physics of Chinese Academy of Sciences (IAP) X-pol for one summer hailstorm over northern China. The results indicate that the identified RH is consistent with the surface hail record of National weather observatory of China Meteorological Administration (CMA), the specific differential phase (KDP) column is more sensitive than the differential reflectivity (ZDR) column for indicating the updraft zone movement, and we considered that the relative position between the key area of hail (KAH, where above the ZDR column, ZDR ~ −2 to 0 dB, ZH ~ ≥ 50 dBZ) and the updraft zone (indicated by the ZDR, KDP column) will determine the evolution of hail. Furthermore, high-density graupels (HDGs) in the upper layer of the KAH may be the source of RH embryos, and these HDGs collect liquid droplets from the liquid water-rich (LWR) area located behind the KAH to form a large number of RH in the lower layer of the KAH. These characteristics are important for hailstone forecasting and warnings.

The Dynamics of Vortex Rossby Waves and Secondary Eyewall Development in Hurricane Matthew (2016): New Insights from Radar Measurements

AbstractThe structure of vortex Rossby waves (VRWs) and their role in the development of a secondary eyewall in Hurricane Matthew (2016) is examined from observations taken during the NOAA Sensing Hazards with Operational Unmanned Technology (SHOUT) field experiment. Radar measurements from ground-based and airborne systems, with a focus on the NASA High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP) instrument on the Global Hawk aircraft, revealed the presence of ~12–15-km-wavelength spiral bands breaking from the inner-core eyewall in the downshear-right quadrant. The vorticity characteristics and calculations of the intrinsic phase speeds of the bands are shown to be consistent with sheared VRWs. A new angular momentum budget methodology is presented that allows an understanding of the secondary eyewall development process with narrow-swath radar measurements. Filtering of the governing equations enables explicit insight into the nonlinear dynamics of scale interactions and the role of the VRWs in the storm structure change. The results indicate that the large-scale (scales > 15 km) vertical flux convergence of angular momentum associated with the VRWs dominates the time tendency with smaller effects from the radial flux term. The small-scale (scales ≤ 15 km) vertical term produces weak, but nonnegligible nonlinear forcing of the large scales primarily through the Reynolds and cross-stress components. The projection of the wave kinematics onto the low-wavenumber (0 and 1) fields appears to be the more significant dynamic process. Flight-level observations show secondary peaks in tangential winds in the radial region where the VRW forcing signatures are active, connecting them with the secondary eyewall formation process.

Evaluation of the Grell–Freitas Convective Scheme in the Hurricane Weather Research and Forecasting (HWRF) Model

Abstract The Developmental Testbed Center (DTC) tested two convective parameterization schemes in the Hurricane Weather Research and Forecasting (HWRF) Model and compared them in terms of performance of forecasting tropical cyclones (TCs). Several TC forecasts were conducted with the scale-aware Simplified Arakawa Schubert (SAS) and Grell–Freitas (GF) convective schemes over the Atlantic basin. For this sample of over 100 cases, the storm track and intensity forecasts were superior for the GF scheme compared to SAS. A case study showed improved storm structure for GF when compared with radar observations. The GF run had increased inflow in the boundary layer, which resulted in higher angular momentum. An angular momentum budget analysis shows that the difference in the contribution of the eddy transport to the total angular momentum tendency is small between the two forecasts. The main difference is in the mean transport term, especially in the boundary layer. The temperature tendencies indicate higher contribution from the microphysics and cumulus heating above the boundary layer in the GF run. A temperature budget analysis indicated that both the temperature advection and diabatic heating were the dominant terms and they were larger near the storm center in the GF run than in the SAS run. The above results support the superior performance of the GF scheme for TC intensity forecast.

Impact of Model Resolution on Tropical Cyclone Simulation Using the HighResMIP–PRIMAVERA Multimodel Ensemble

AbstractA multimodel, multiresolution set of simulations over the period 1950–2014 using a common forcing protocol from CMIP6 HighResMIP have been completed by six modeling groups. Analysis of tropical cyclone performance using two different tracking algorithms suggests that enhanced resolution toward 25 km typically leads to more frequent and stronger tropical cyclones, together with improvements in spatial distribution and storm structure. Both of these factors reduce typical GCM biases seen at lower resolution. Using single ensemble members of each model, there is little evidence of systematic improvement in interannual variability in either storm frequency or accumulated cyclone energy as compared with observations when resolution is increased. Changes in the relationships between large-scale drivers of climate variability and tropical cyclone variability in the Atlantic Ocean are also not robust to model resolution. However, using a larger ensemble of simulations (of up to 14 members) with one model at different resolutions does show evidence of increased skill at higher resolution. The ensemble mean correlation of Atlantic interannual tropical cyclone variability increases from ~0.5 to ~0.65 when resolution increases from 250 to 100 km. In the northwestern Pacific Ocean the skill keeps increasing with 50-km resolution to 0.7. These calculations also suggest that more than six members are required to adequately distinguish the impact of resolution within the forced signal from the weather noise.

The Quality Control and Rain Rate Estimation for the X-Band Dual-Polarization Radar: A Study of Propagation of Uncertainty

In this study, the specific differential phase ( K d p ) is applied to attenuation correction for radar reflectivity Z H and differential reflectivity Z D R , and then the corrected Z H , Z D R , and K d p are studied in the rain rate (R) estimation at the X-band. The statistical uncertainties of Z H , Z D R , and R are propagated from the uncertainty of K d p , leading to variability in their error characteristics. For the attenuation correction, a differential phase shift ( Φ d p )-based method is adopted, while the statistical uncertainties σ ( Z H ) and σ ( Z D R ) are related to σ ( K d p ) via the relations of K d p -specific attenuation ( A H ) and K d p -specific differential attenuation ( A D P ), respectively. For the rain rate estimation, the rain rates are retrieved by the power-law relations of R ( K d p ) , R ( Z h ) , R ( Z h , Z d r ) , and R ( Z h , Z d r , K d p ) . The statistical uncertainty σ ( R ) is propagated from Z H , Z D R , and K d p via the Taylor series expansion of the power-law relations. A composite method is then proposed to reduce the σ ( R ) effect. When compared to the existing algorithms, the composite method yields the best performance in terms of root mean square error and Pearson correlation coefficient, but shows slightly worse normalized bias than R ( K d p ) and R ( Z h , Z d r , K d p ) . The attenuation correction and rain rate estimation are evaluated by analyzing a squall line event and a prolonged rain event. It is clear that Z H , Z D R , and K d p show the storm structure consistent with the theoretical model, while the statistical uncertainties σ ( Z H ) , σ ( Z D R ) and σ ( K d p ) are increased in the transition region. The scatterplots of Z H , Z D R , and K d p agree with the self-consistency relations at X-band, indicating a fairly good performance. The rain rate estimation algorithms are also evaluated by the time-series of the prolonged rain event, yielding strong correlations between the composite method and rain gauge data. In addition, the statistical uncertainty σ ( R ) is propagated from Z H , Z D R , and K d p , showing higher uncertainty when the large gradient presents.

Real Time Anomaly Detection Techniques Using PySpark Frame Work

The identification of anomaly in a network is a process of observing keenly the minute behavioral changes from the usual pattern followed. These are often referred with different names malware, exceptions, and anomaly or as outlier according to the dominion of the application. Though many works have emerged for the detection of the outlier, the identification of the abnormality in the multiple source data stream structure is still under research. To identify the abnormalities in the cloud data center that is encompassed with the multiple-source VMWare, by observing the behavioral changes in the load of the CPU, utilization of the memory etc. consistently the paper has developed a real time identification process. The procedure followed utilizes the PySpark to compute the batches of data and make predictions, with minimized delay. Further a flat-increment based clustering is used to frame the normal attributes in the PySpark Structure. The latencies in computing the tuple while clustering and predicting, was compared for PySpark, Storm and other dispersed structure that were used in processing the batches of data and was experimentally found that the processing time of tuple in a PySpark was much lesser compared to the other methods.

STUDY ON SENSITIVITY OF WIND FIELD VARIATION TO STRUCTURE AND DEVELOPMENT OF CONVECTIVE STORMS

In order to study the impacts of wind field variations in the middle and lower troposphere on the development and structure of storms, we carried out numerical experiments on cases of severe convection in the Jianghuai area under the background of cold vortex on April 28, 2015. The results show that the structure and development of convective storms are highly sensitive to the changes of wind fields, and the adjustment of wind fields in the middle or lower troposphere will lead to significant changes in the development and structure of storms. When the wind field in the middle or lower troposphere is weakened, the development of convective storms attenuates to some extent compared with that in the control experiment, and the ways of attenuation in the two experiments are different. In the attenuation test of wind field at the middle level, convective storms obviously weaken at all stages in its development, while for the wind field at the low level, the convective storms weaken only in the initial stage of storm. On the contrary, the enhancement of the wind field in the middle or lower troposphere is conducive to the development of convection, especially the enhancement in the middle troposphere. In contrast, the convective storms develop rapidly in this test, as the most intensive one. The wind field variations have significant impacts on the structure and organization of the storm. The enhancement of wind field in the middle troposphere facilitates the intension of the middle-level rotation in convective storm, the reduction of the storm scale, and the organized evolution of convective storms. The strengthening of the wind field in the lower troposphere is conducive to the development of the low-level secondary circulation of the storm and the cyclonic vorticity at the middle and low levels on the inflowing side of the storms.

Response of Extreme Rainfall for Landfalling Tropical Cyclones Undergoing Extratropical Transition to Projected Climate Change: Hurricane Irene (2011).

Extreme rainfall and flooding associated with landfalling tropical cyclones (TCs) have large societal impacts, both in fatalities and economic losses. This study examines the response of TC rainfall to climate change projected under future anthropogenic greenhouse emissions, focusing on Hurricane Irene, which produced severe flooding across the Northeastern United States in August 2011. Numerical simulations are made with the Weather Research and Forecasting model, placing Irene in the present‐day climate and one projected for the end of 21st century climate represented by Phase 5 of the Coupled Model Intercomparison Project Representative Concentration Pathway 8.5 scenario. Projected future changes to surface and atmospheric temperature lead to a storm rainfall increase of 32% relative to the control run, exceeding the rate expected by the Clausius‐Clapeyron relation given a ~3‐K lower atmospheric warming. Analyses of the atmospheric water balance highlight contributions to the increase in rainfall rate from both increased circulation strength and atmospheric moisture. Storm rainfall rate shows contrasting response to global warming during TC and extratropical transition periods. During the TC phase, Irene shows a significant increase of storm rainfall rate in inner core regions. This increase shifts to outer rainbands as Irene undergoes extratropical transition, collocated with the maximum tangential wind increase and the change of secondary circulation strength. Changes of storm track from the control run to global warming projections play a role in the change of spatial rainfall pattern. Distinct roles of surface and atmospheric warming in storm rainfall and structure changes are also examined.

Satellite-observed warm-core structure in relation to tropical cyclone intensity change

Using a 13-year dataset of Atmospheric Infrared Sounder (AIRS) retrieved temperature profiles including 5019 AIRS overpasses in 1061 tropical storm through category-2 tropical cyclones (TCs) in global basins during 2002–2014, this study examines the relationship between the warm-core structure and TC intensity change with a focus on rapid intensification (RI). The AIRS TC overpasses are classified into RI, slowly intensifying (SI), neutral (N), and weakening (W) categories. The effect of the warm-core structure upon TC intensification is entangled with that upon TC intensity. It is necessary to exclude the weakening category in order to single out the relationship between TC intensification and warm-core structure from a statistical method. The composite warm-core maximum temperature anomaly is the strongest in RI storms (~7 K), followed by W (~6 K), SI (~5 K) and N (~ 4 K) storms. RI storms have the highest equivalent potential temperature ( θ e ) and CAPE in the eye among all intensity change categories. The warm-core structure of RI storms is asymmetric relative to shear, with the higher temperature anomaly and convective available potential energy (CAPE) located in the down-shear quadrant. When only considering samples with intensification rates ≥0, a significant and positive correlation is found between the warm-core strength and TC intensification rate. The warm-core height is also positively correlated with the TC intensification rate at a high confidence level. The AIRS-derived warm-core temperature anomaly greater than 4 K and weighted warm-core height higher than 450 hPa are the necessary conditions for RI. • Warm-core strength is positively correlated with TC intensification rate. • Warm-core height is positively correlated with TC intensification rate. • RI never happens when warm-core temperature anomaly less than 4 K. • RI never happens when weighted warm-core height lower than 450 hPa.

Eye of the Storm: Observing Hurricanes with a Small Unmanned Aircraft System

AbstractUnique data from seven flights of the Coyote small unmanned aircraft system (sUAS) were collected in Hurricanes Maria (2017) and Michael (2018). Using NOAA’s P-3 reconnaissance aircraft as a deployment vehicle, the sUAS collected high-frequency (>1 Hz) measurements in the turbulent boundary layer of hurricane eyewalls, including measurements of wind speed, wind direction, pressure, temperature, moisture, and sea surface temperature, which are valuable for advancing knowledge of hurricane structure and the process of hurricane intensification. This study presents an overview of the sUAS system and preliminary analyses that were enabled by these unique data. Among the most notable results are measurements of turbulence kinetic energy and momentum flux for the first time at low levels (<150 m) in a hurricane eyewall. At higher altitudes and lower wind speeds, where data were collected from previous flights of the NOAA P-3, the Coyote sUAS momentum flux values are encouragingly similar, thus demonstrating the ability of an sUAS to measure important turbulence properties in hurricane boundary layers. Analyses from a large-eddy simulation (LES) are used to place the Coyote measurements into context of the complicated high-wind eyewall region. Thermodynamic data are also used to evaluate the operational HWRF model, showing a cool, dry, and thermodynamically unstable bias near the surface. Preliminary data assimilation experiments also show how sUAS data can be used to improve analyses of storm structure. These results highlight the potential of sUAS operations in hurricanes and suggest opportunities for future work using these promising new observing platforms.

Analysis of an Ensemble of High-Resolution WRF Simulations for the Rapid Intensification of Super Typhoon Rammasun (2014)

Diagnostics are presented from an ensemble of high-resolution forecasts that differed markedly in their predictions of the rapid intensification (RI) of Typhoon Rammasun. We show that the basic difference stems from subtle differences in initializations of (a) 500–850-hPa environmental winds, and (b) midlevel moisture and ventilation. We then describe how these differences impact on the evolving convective organization, storm structure, and the timing of RI. As expected, ascent, diabatic heating and the secondary circulation near the inner-core are much stronger in the member that best forecasts the RI. The evolution of vortex cloudiness from this member is similar to the actual imagery, with the development of an inner cloud band wrapping inwards to form the eyewall. We present evidence that this structure, and hence the enhanced diabatic heating, is related to the tilt and associated dynamics of the developing inner-core in shear. For the most accurate ensemble member: (a) inhibition of ascent and a reduction in convection over the up-shear sector allow moistening of the boundary-layer air, which is transported to the down-shear sector to feed a developing convective asymmetry; (b) with minimal ventilation, undiluted clouds and moisture from the down-shear left quadrant are then wrapped inwards to the up-shear left quadrant to form the eyewall cloud; and (c) this process seems related to a critical down-shear tilt of the vortex from midlevels, and the vertical phase-locking of the circulation over up-shear quadrants. For the member that forecasts a much-delayed RI, these processes are inhibited by stronger vertical wind shear, initially resulting in poor vertical coherence of the circulation, lesser moisture and larger ventilation. Our analysis suggests that ensemble prediction is needed to account for the sensitivity of forecasts to a relatively narrow range of environmental wind shear, moisture and vortex inner-structure.

Convolutional Neural Network for Convective Storm Nowcasting Using 3-D Doppler Weather Radar Data

Convective storms are one of the severe weather hazards found during the warm season. Doppler weather radar is the only operational instrument that can frequently sample the detailed structure of convective storm which has a small spatial scale and short lifetime. For the challenging task of short-term convective storm forecasting, 3-D radar images contain information about the processes in convective storm. However, effectively extracting such information from multisource raw data has been problematic due to a lack of methodology and computation limitations. Recent advancements in deep learning techniques and graphics processing units now make it possible. This article investigates the feasibility and performance of an end-to-end deep learning nowcasting method. The nowcasting problem was transformed into a classification problem first, and then, a deep learning method that uses a convolutional neural network was presented to make predictions. On the first layer of CNN, a cross-channel 3D convolution was proposed to fuse 3D raw data. The CNN method eliminates the handcrafted feature engineering, i.e., the process of using domain knowledge of the data to manually design features. Operationally produced historical data of the Beijing-Tianjin-Hebei region in China was used to train the nowcasting system and evaluate its performance; 3737332 samples were collected in the training data set. The experimental results show that the deep learning method improves nowcasting skills compared with traditional machine learning methods.

Model-Based Probable Maximum Precipitation Estimation: How to Estimate the Worst-Case Scenario Induced by Atmospheric Rivers?

Abstract The concept of probable maximum precipitation (PMP) is widely used for the design and risk assessment of water resource infrastructure. Despite its importance, past attempts to estimate PMP have not investigated the realism of design maximum storms from a meteorological perspective. This study investigates estimating PMP with realistically maximized storms in a Pacific Northwest region dominated by atmospheric rivers (ARs) using numerical weather models (NWMs). The moisture maximization and storm transposition methods used in NWM-based PMP estimates are examined. We use integrated water vapor transport as a criterion to modify water vapor only at the modeling boundary crossing the path of ARs, whereas existing methods maximize relative humidity at all initial/boundary conditions. It is found that saturation of the entire modeling boundaries can produce unrealistic atmospheric conditions and does not necessarily maximize precipitation over a watershed due to storm structure, stability, and topography. The proposed method creates more realistic atmospheric fields and more severe precipitation. The simultaneous optimization of moisture content and location of storms is also considered to rigorously estimate the most extreme precipitation. Among the 20 most severe storms during 1980–2016, the AR event during 5–9 February 1996 produces the largest 72-h basin-average precipitation when maximized with our method (defined as PMP of this study), in which precipitation is intensified by 1.9 times with a 0.7° shift south and a 30% increase in AR moisture. The 24-, 48-, and 72-h PMP estimates are found to be at least 70 mm lower than the Hydrometeorological Reports estimates regardless of duration.

Exploring the Potential Impact of Louisville's Urban Heat Island on the Structure and Intensity of an Organized Convective Systems

Simulations of an organized convective system interacting with Louisville, KY were conducted to determine the impact of urban heat island (UHI) magnitude on the structure and intensity of convective storms. Four different simulations are presented: A control, two simulations in which the UHI is enhanced through observational nudging, and a simulation where the urban area is removed (no-city simulation). Results show that the downwind enhancement of convective cells increases with increasing UHI magnitude. However, large differences are also apparent between the control and the no-city simulations. This suggests that surface convergence caused by flow obstruction may be as influential to the modification of organized storms as the UHI, although the locations of these two effects are offset. Convergence due to flow obstruction is most noticeable on the upwind edge and over the center of the city while the impact of UHI magnitude is most noticeable on the downwind edge of the city.

Bias Correction of Tropical Cyclone Parameters in the ECMWF Ensemble Prediction System in Australia

Abstract Global ensemble prediction systems have considerable ability to predict tropical cyclone (TC) formation and subsequent evolution. However, because of their relatively coarse resolution, their predictions of intensity and structure are biased. The biases arise mainly from underestimated intensities and enlarged radii, in particular the radius of maximum winds. This paper describes a method to reduce this limitation by bias correcting TCs in the ECMWF Ensemble Prediction System (ECMWF-EPS) for a region northwest of Australia. A bias-corrected TC system will provide more accurate forecasts of TC-generated wind and waves to the oil and gas industry, which operates a large number of offshore facilities in the region. It will also enable improvements in response decisions for weather sensitive operations that affect downtime and safety risks. The bias-correction technique uses a multivariate linear regression method to bias correct storm intensity and structure. Special strategies are used to maintain ensemble spread after bias correction and to predict the radius of maximum winds using a climatological relationship based on wind intensity and storm latitude. The system was trained on the Australian best track TC data and the ECMWF-EPS TC data from two cyclone seasons. The system inserts corrected vortices into the original surface wind and pressure fields, which are then used to estimate wind exceedance probabilities, and to drive a wave model. The bias-corrected system has shown an overall skill improvement over the uncorrected ECMWF-EPS for all TC intensity and structure parameters with the most significant gains for the maximum wind speed prediction. The system has been operational at the Australian Bureau of Meteorology since November 2016.