Storm Size Research Articles

Onset of Tropical Cyclone Rapid Intensification: Evaluating the Response to Length Scales of Sea Surface Temperature Anomalies

Abstract Predicting the rapid intensification (>15.0 m s−1 increase in 10 m wind speed over 24 h or less) of tropical cyclones (TC) remains a challenge in the broader context of numerical weather prediction largely due to their multiscale dynamics. Ocean observations show that the size and magnitude of sea surface temperature (SST) anomalies associated with cold wakes and ocean eddies play important roles in TC dynamics. In this study, a combination of spectral and structure function analyses is utilized to generate realistic realizations of multiscale anomalies characteristic of the SST conditions in which Hurricane Irma (2017) underwent rapid intensification (RI). We investigate the impact of the length scale of these SST anomalies and the role of translation speed on the variance in RI onset timing. Length-scale-induced convective asymmetries, in addition to the mean magnitude of SST anomalies beneath the storm eye, are shown to modulate the variance in RI onset timing. The size of the associated SST length scales relative to the storm size is critical to the magnitude of variance in RI onset timing, as smaller length scales are shown to lack the spatial extent required to induce preferential convective asymmetries. Storm translation speed is also shown to influence the variance in RI onset timing for larger-length-scale ensembles by altering the exposure time of the eye to these SST anomalies. We find that an interplay between SST-induced convective asymmetries, the magnitude of SST anomalies underneath the eye/eyewall, and storm translation speed play crucial roles in modulating the variance in RI onset timing. Significance Statement The characteristics of sea surface temperature (SST) anomalies in the tropical cyclone near-environment are inherently multiscale in nature as a result of interactions between various dynamical processes in the ocean. Assuming a uniform SST beneath storms in numerical simulations limits the predictability of how air–sea interaction affects the physics of rapid intensification (RI). In this study, the influence of realistic multiscale SST anomalies on RI onset timing is investigated. Our results suggest that the length scale of SST anomalies (in addition to its magnitude) modulate the distribution of convection, creating asymmetries around the RMW that can influence the predictability of RI onset. This effect is further modulated by storm translation speed, with the most prominent impact seen in slow-moving storms.

Understanding the Effects of Wind Intensity, Forward Speed, and Wave on the Propagation of Hurricane Harvey Surges

Hurricane storm surges are influenced by wind intensity, forward speed, width and slope of the ocean bottom, central pressure, angle of approach, shape of coastal lines, local features, and storm size. A numerical experiment is conducted using the Advanced Circulation + Simulation and Simulating Waves Nearshore (ADCIRC + SWAN) coupled model for understanding the effects of wind intensity, forward speed, and wave on the storm surges caused by Hurricane Harvey. The ADCIRC + SWAN is used to simulate hurricane storm surges and waves. The wind fields of Hurricane Harvey were reconstructed from observed data, aided by a variety of methodologies and analyses conducted by Ocean Weather Inc (OWI) after the event. These reconstructed wind fields were used as the meteorological forcing in the base case in ADCIRC+SWAN to investigate the storm surges caused by the hurricane. Hurricane Harvey was the second most costly hurricane in the United States, causing severe urban flooding by dropping more than 60 inches of rainfall in Texas. The hurricane made three landfalls, with its first landfall as a Category 4 based on the Saffir–Simpson Hurricane Wind Scale (SSHWS), with wind intensities of 212.98 km/h (59 m/s). The storm surges caused by Hurricane Harvey were unique due to the slow speed, crooked tracks, triple landfalls in the USA, and excessive rain. The model’s storm surge and wave results were compared against observed data. High water marks at 21 locations and time series at 12 National Oceanic and Atmospheric Administration (NOAA) gauges were compared with the generated results. Several cases were investigated by increasing or decreasing the wind intensity or hurricane forward speed by 25% of the OWI wind and pressure data. The effects of the wave were analyzed by comparing the results obtained from ADCIRC + SWAN (with waves) and ADCIRC (without waves) models. The study found that the changes in wind intensity had the most significant effect on storm surges, followed by wave and forward speed changes. This study signifies the importance of considering these factors to enhance accuracy in predicting storm surges.

A new point cloud processing method unveiled hidden coastal boulders from deep vegetation

Huge coastal boulders are useful to reconstruct the size of past extreme waves such as those associated with tsunamis and storms using inverse-type or forward-type boulder transport models. These models fundamentally require the precise shape of boulders. Traditionally, they have often been assumed to be rectangular or ellipsoidal with three axes measured in the field. However, if the boulder’s shape is complex, this method is unable to represent the actual shape accurately. Therefore, it prevents estimation of the tsunami or storm size reasonably using models. For this reason, boulders have recently been surveyed using 3D scanning techniques such as LiDAR. However, coastal boulders now on land in tropical and subtropical areas such as Japan and Tonga are often covered by deep vegetation, which makes 3D surveys difficult. This report presents new methods to ascertain boulder shapes when they are obscured by vegetation. First, using UAV-type and mobile-type LiDAR, we scanned well-known tsunami boulders in southwestern Japan that had been covered with deep vegetation. Then, we developed a new method to extract only boulders and filter out vegetation from a point cloud. Thereby, we created 3D models of the boulders. We improved the boulder transport model further to assume the 3D boulder model accurately. In addition to coastal boulders, this filtering method is expected to be useful for unveiling any object, such as an archaeological structure, that is hidden in deep vegetation.

A multi-year analysis of factors affecting ecosystem metabolism in forested and meadow reaches of a Piedmont stream

Studies of stream ecosystem metabolism over decades are rare and focused on responses to a single factor, e.g., nutrient reduction or storms. Numerous studies document that light, temperature, allochthonous inputs, nutrients, and flow affect metabolism. We use measurements spanning ~ 40 years to examine the interplay of all these influences on metabolism in forested and meadow reaches of a rural stream in southeastern Pennsylvania, USA. Measurements made in 1971–1975 used benthic substrata transferred to chambers (Period 1, P1), and ones in 1997–2010 used open system methodology (P2). Metabolism was greater in the Meadow reach both periods. Gross primary productivity (GPP) was driven primarily by light and chlorophyll, and respiration (R) by temperature and inclusion of hyporheic metabolism. Annually, processes were nearly balanced (Forested reach) or dominated by autotrophy (Meadow reach) in P1. Heterotrophy predominated in both reaches in P2, fueled by litter inputs (Forested reach) and fine particulate organic matter from the agricultural watershed (Meadow reach). Storms reduced GPP, R, and chlorophyll in proportion to storm size, but had less influence than other environmental factors. Riparian-zone reforestation of the P1 Meadow reach resulted in incident light and GPP similar to that in the permanent Forested reach within ~ 20 years.

Scaling for Saturated Moist Quasigeostrophic Turbulence

Abstract Much of our conceptual understanding of midlatitude atmospheric motion comes from two-layer quasigeostrophic (QG) models. Traditionally, these QG models do not include moisture, which accounts for an estimated 30%–60% of the available energy of the atmosphere. The atmospheric moisture content is expected to increase under global warming, and therefore, a theory for how moisture modifies atmospheric dynamics is crucial. We use a two-layer moist QG model with convective adjustment as a basis for analyzing how latent heat release and large-scale moisture gradients impact the scalings of a midlatitude system at the synoptic scale. In this model, the degree of saturation can be tuned independently of other moist parameters by enforcing a high rate of evaporation from the surface. This allows for study of the effects of latent heat release at saturation, without the intrinsic nonlinearity of precipitation. At saturation, this system is equivalent to the dry QG model under a rescaling of both length and time. This predicts that the most unstable mode shifts to smaller scales, the growth rates increase, and the inverse cascade extends to larger scales. We verify these results numerically and use them to verify a framework for the complete energetics of a moist system. We examine the spectral features of the energy transfer terms. This analysis shows that precipitation generates energy at small scales, while dry dynamics drive a significant broadening to larger scales. Cascades of energy are still observed in all terms, albeit without a clearly defined inertial range. Significance Statement The effect of moist processes, especially the impact of latent heating associated with condensation, on the size and strength of midlatitude storms is not well understood. Such insight is particularly needed in the context of global warming, as we expect moisture to play a more important role in a warmer world. In this study, we provide intuition into how including condensation can result in midlatitude storms that grow faster and have features on both larger and smaller scales than their dry counterparts. We provide a framework for quantifying these changes and verify it for the special case where it is raining everywhere. These findings can be extended to the more realistic situation where it is only raining locally.

When impervious cover doesn't predict urban runoff: Lessons from distributed overland flow modeling

We used the distributed hydrologic model GSSHA to simulate overland runoff from impervious and semi-pervious (low vegetation) land covers in a 0.3 km2 urban catchment. The model is hypothetical, which allowed us to test alternative land use configurations and better understand the effects of urban heterogeneity. To track the arrival of water to a stormwater basin inlet from distinct parts of the catchment, we applied a conservative solute tracer to portions of the gridded model area, a new application for GSSHA’s ‘contaminant transport’ module. We rearranged the positions of pervious and impervious land uses over the catchment during four storm events and found that the portion of total runoff volume from impervious areas varied from 50 to 75% while the relative proportion of impervious cover remained constant at 54%. We also applied solute to equal-sized zones with different slopes and land uses and found that percent imperviousness did not predict solute mobilization. Furthermore, variation in total runoff volume and peak discharge for different land use arrangements and storm sizes showed that percent imperviousness could not be used to estimate a runoff-rainfall ratio. Fluctuating solute proportions over time suggested that grab samples might not be adequate for capturing average overland runoff chemistry. We quantified factors contributing to non-linearity in rainfall-runoff responses from an urban watershed with stormwater infrastructure and determined that using models to find hotspots for stormwater infiltration and to explore resilience to future storm events can improve design and function of stormwater control measures.

A Model for the Tropical Cyclone Wind Field Response to Idealized Landfall

Abstract The impacts of a tropical cyclone after landfall depend not only on storm intensity but also on the size and structure of the wind field. Hence, a simple predictive model for the wind field after landfall has significant potential value. This work tests existing theory for wind structure and size over the ocean against idealized axisymmetric landfall experiments in which the surface beneath a mature storm is instantaneously dried and roughened individually or simultaneously. Structure theory captures the response of the low-level wind field to different types of idealized landfalls, given the intensity and size response. Storm size, modeled to follow the ratio of simulated time-dependent storm intensity to the Coriolis parameter , can generally predict the transient response of the storm gale wind radii r34kt to inland surface forcings, particularly for at least moderate surface roughening regardless of the level of drying. Given knowledge of the intensity evolution, the above results combine to yield a theoretical model that can predict the full tangential wind field response to idealized landfalls. Significance Statement A theoretical model that can predict the time-dependent wind field structure of landfalling tropical cyclones (TCs) with a small number of physical, observable input parameters is essential for mitigating hazards and allocating public resources. This work provides a first-order prediction of storm size and structure after landfall, which can be combined with existing intensity predictions to form a simple model describing the inland wind field evolution. Results show its potential utility for modeling idealized inland TC wind fields.

Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone–Driven Storm Tides and Inundation

Abstract This study proposes and assesses a methodology to obtain high-quality probabilistic predictions and uncertainty information of near-landfall tropical cyclone–driven (TC-driven) storm tide and inundation with limited time and resources. Forecasts of TC track, intensity, and size are perturbed according to quasi-random Korobov sequences of historical forecast errors with assumed Gaussian and uniform statistical distributions. These perturbations are run in an ensemble of hydrodynamic storm tide model simulations. The resulting set of maximum water surface elevations are dimensionality reduced using Karhunen–Loève expansions and then used as a training set to develop a polynomial chaos (PC) surrogate model from which global sensitivities and probabilistic predictions can be extracted. The maximum water surface elevation is extrapolated over dry points incorporating energy head loss with distance to properly train the surrogate for predicting inundation. We find that the surrogate constructed with third-order PCs using elastic net penalized regression with leave-one-out cross validation provides the most robust fit across training and test sets. Probabilistic predictions of maximum water surface elevation and inundation area by the surrogate model at 48-h lead time for three past U.S. landfalling hurricanes (Irma in 2017, Florence in 2018, and Laura in 2020) are found to be reliable when compared to best track hindcast simulation results, even when trained with as few as 19 samples. The maximum water surface elevation is most sensitive to perpendicular track-offset errors for all three storms. Laura is also highly sensitive to storm size and has the least reliable prediction. Significance Statement The purpose of this study is to develop and evaluate a methodology that can be used to provide high-quality probabilistic predictions of hurricane-induced storm tide and inundation with limited time and resources. This is important for emergency management purposes during or after the landfall of hurricanes. Our results show that sampling forecast errors using quasi-random sequences combined with machine learning techniques that fit polynomial functions to the data are well suited to this task. The polynomial functions also have the benefit of producing exact sensitivity indices of storm tide and inundation to the forecasted hurricane properties such as path, intensity, and size, which can be used for uncertainty estimation. The code implementing the presented methodology is publicly available on GitHub.

North Atlantic Tropical Cyclone Outer Size and Structure Remain Unchanged by the Late Twenty-First Century

Abstract There is a lack of consensus on whether North Atlantic tropical cyclone (TC) outer size and structure (i.e., change in outer winds with increasing radius from the TC) will differ by the late twenty-first century. Hence, this work seeks to examine whether North Atlantic TC outer wind field size and structure will change by the late twenty-first century using multiple simulations under CMIP3 SRES A1B and CMIP5 RCP4.5 scenarios. Specifically, our analysis examines data from the GFDL High-Resolution Forecast-Oriented Low Ocean Resolution model (HiFLOR) and two versions of the GFDL hurricane model downscaling climate model output. Our results show that projected North Atlantic TC outer size and structure remain unchanged by the late twenty-first century within nearly all HiFLOR and GFDL hurricane model simulations. Moreover, no significant regional outer size differences exist in the North Atlantic within most HiFLOR and GFDL hurricane model simulations. No changes between the control and late-twenty-first-century simulations exist over the storm life cycle in nearly all simulations. For the simulation that shows significant decreases in TC outer size, the changes are attributed to reductions in storm lifetime and outer size growth rates. The absence of differences in outer size among most simulations is consistent with the process that controls the theoretical upper bound of storm size (i.e., Rhines scaling), which is thermodynamically invariant. However, the lack of complete consensus among simulations for many of these conclusions suggests nontrivial uncertainty in our results.

Effects of Storm Size on the Interactions between Mid-Latitude Westerlies and Tropical Cyclones during Extratropical Transition in the Western North Pacific

Effects of Storm Size on the Interactions between Mid-Latitude Westerlies and Tropical Cyclones during Extratropical Transition in the Western North Pacific

Effects of Design and Climate on Bioretention Effectiveness for Watershed-Scale Hydrologic Benefits

Bioretention areas are a common form of green stormwater infrastructure (GSI). There is significant research on the performance of individual bioretention cells, but the watershed-scale benefits of GSI are still unclear. Furthermore, differences in bioretention design and rainfall patterns make it difficult to compare results between studies. We used the Storm Water Management Model (SWMM) to assess the effects of bioretention size, soil infiltration rate, storm size, and climate on the watershed-scale performance of GSI. We first divided the contiguous US into 10 rainfall regions based on similarities in precipitation amount, intensity, and other storm characteristics. We then modeled the effects of bioretention areas in a single watershed under these different rainfall regimes. Bioretention areas did provide watershed-scale benefits, although performance declined as (1) bioretention areas became smaller, (2) soil infiltration rates decreased, and (3) precipitation depth increased. High-intensity rainfall was the primary cause of outflow from bioretention areas, although back-to-back storm events also caused outflow in some climates. There were some clear discrepancies between subbasin-scale and watershed-scale GSI performance. Generally, runoff volume reduction was greater when measured at the subbasin scale. Peak flow reduction, however, was greater at the watershed-scale, likely because bioretention areas changed the shape of subbasin runoff hydrographs, leading to watershed-scale peak flow reduction that was greater than the sum of the parts. We provide recommendations for design, management, and future research to help advance effective application of GSI for achieving watershed-scale hydrologic benefits.

Stream nitrogen uptake associated with suspended sediments: A microcosm study

Despite significant advances in our understanding of nitrogen (N) removal pathways along river networks, the role of water column processes remains largely understudied. This knowledge gap not only limits our capacity to determine N transport and retention in mid-to-large rivers but also hampers our understanding of N removal processes in smaller streams during stormflow conditions, in which significant increases in suspended sediment concentrations (SSC) typically occur. High SSC in the water column can provide abundant substrate for microbial growth and water column N uptake. However, storms of different size mobilize different quantities of sediment of varying properties and sizes, which can ultimately modulate water column N uptake rates in the stream during stormflows. To assess water column N uptake associated with suspended sediment particles of different sources and sizes, we quantified assimilatory and dissimilatory N uptake rates in a set of microcosms representing a gradient of sediment properties (organic matter, N content, and microbial activity) and surface area (fine vs. coarse size) availability. Water column assimilatory uptake (Used) ranged from 12.7 to 187.8 µg N [g sediment]−1 d−1 across all sediment sources and size fractions, and was higher on average than denitrification rates (DNsed) in agricultural and stream bank sediments but not in streambed sediments (mean DNsed = 240.9 ± 99 µg·N [g sediment]−1·d−1). Sediment-bound C in suspended sediment varied among sediment sources and was directly related to Used rates, but not to DNsed rates, which were less predictable and more variable. Overall, our results showed a positive nonlinear relationship between water column N uptake and SSC, while indicating that water column N uptake may scale differently to SSC depending on sediment source, and to a lesser degree, particle size. Because low, moderate, and large storms can mobilize different quantities of sediment in the watershed of different sources and sizes, it is likely that storm size will ultimately modulate the contribution of water column uptake during storm events to whole-reach N retention.

A historical re-analysis of the calamitous midget typhoon passing through Hong Kong on 18 September 1906 and its storm surge impact to Hong Kong

A typhoon passed through Hong Kong suddenly and unexpectedly on 18 September 1906 (the “Typhoon 1906”) and had a disastrous impact on Victoria Harbour and its surroundings in just a couple of hours. Since the year 1906 was the “Bingwu” year in the Chinese calendar, the typhoon is also known historically as the “Bingwu Typhoon”. Tremendous loss of lives and property resulted, and the shipping and fishing communities were devastated. Two mysteries arising from this calamitous typhoon have existed to date: 1) Why the Hong Kong Observatory was not able to provide any forewarning? 2) whether the storm surge reported in some contemporary records is entirely credible? This paper will focus on both of these.In this paper, we re-analyse historical weather observations recorded in various historical documents and estimate the possible storm size, intensity and track of Typhoon 1906 using tropical cyclone models. Based on the re-analyses, the storm surges, storm tides and wave heights in Hong Kong are also estimated using storm surge and wave models. The results reveal that Typhoon 1906 was a midget typhoon, with a radius of maximum winds of 11 km or smaller, during its passage through Hong Kong. This explains why it was technically impossible for a forewarning to be given at that time when real-time weather observations from ships, meteorological satellites and radars were non-existent. We also estimate that the maximum storm surges (storm tides) in Hong Kong were not higher than 0.82 m (2.43 mCD) and 1.98 m (4.15 mCD) in Victoria Harbour and Tolo Harbour respectively. These figures are found to be limited by the intensity and the storm size of the typhoon. Therefore, we conclude that the previously documented storm surge figures are not supported by the present study.

Tropical Cyclone Intensification and Fullness: The Role of Storm Size Configuration

AbstractThe relationships between the intensification rate (IR) and the size configuration of tropical cyclones (TCs) are investigated. Theoretically, the intensity, size configuration, and relative vorticity of TCs are intrinsically linked. TC fullness (TCF) is used to quantify the size configuration of TCs and can be expressed as the product of the critical fullness (TCF0) and the ratio of fullness (Rf). TCF0 represents the fundamental constraint on size configuration shared by TCs at a given intensity and is the lower limit of fullness, whereas Rf reveals a TC's unique wind structural feature. Intermediate TCF0 (∼0.55), moderate Rf (∼1.4), and thus high TCF (∼0.8) collectively facilitate the occurrence of a large IR of TC. Obtaining a stable and moderate Rf at low TCF0 favors the subsequent intensification of a TC. The TCF‐TCF0 diagram is proposed to illustrate the co‐evolution of TC size configuration and intensity, and to reveal how they modulate intensification.

Variability of Environmental Conditions for Tropical Cyclone Rapid Intensification in the Western North Pacific

Abstract How environmental conditions vary among rapidly intensifying tropical cyclones (TCs) and which factors can help offset negative factors for intensification were examined using a dataset of geostationary satellites and environmental diagnostics. The dataset contains TCs in the western North Pacific from 1995 to 2020. A cluster analysis was performed to classify different morphologies of TC cloud patterns at the onset of rapid intensification (RI). Six clusters were identified, and each cluster had a distinct set of environmental conditions. Three clusters (clusters 1, 3, and 5) had some conditions unfavorable for RI. Cluster 1 TCs were exposed to moderate vertical (850–200 hPa) shear (∼6 m s−1). Relatively high sea surface temperature, a moist environment, and movement toward environments with weak vertical shear, high equivalent potential temperature, and high ocean heat content are potential factors that resist the effects of vertical shear. Cluster 3 TCs were characterized by a large 30-kt wind radius and moderate vertical shear (1 kt ≈ 0.51 m s−1). Large storm size and a moist environment caused by large-scale, strong, low-level convergence are possible factors for vortex resiliency against shear. Cluster 5 TCs were located in a very dry environment. Weak vertical shear and small storm size are factors that may offset the negative effects of dry air and ocean cooling. The results suggest that in the case of RI with negative conditions for intensification, other factors can offset the negative impacts of those conditions and that suitable combinations of environmental conditions and TC structural features are important for RI.

Examining How a Smart Rainwater Harvesting System Connected to a Green Roof Can Improve Urban Stormwater Management

This study quantified how a smart rainwater harvesting cistern that collected stormwater runoff from a green roof reduced stormwater flow into a combined sewer system (CSS) during wet-weather flow. The studied smart rainwater harvesting cistern collected runoff from a green roof located in Bronx, New York City; it used the Continuous Monitoring and Adaptive Control (CMAC) smart sensor provided by OptiRTC, Inc., to regulate the water flow from the cistern. The cistern collected stormwater runoff from the roof, usually draining completely after 24 h of dry weather. However, the smart sensor used weather forecasting data, and if additional rainfall was predicted immediately following another storm, the cistern only drained a specific amount, calibrated to mitigate the CSO. Five years of data from the cistern system were used to understand the role of the cistern’s smart sensor in reducing stormwater flow into the CSS during storms. The study results demonstrate that connecting the smart cistern system to the green-roof maximized stormwater collection (compared with the green roof alone) for storm sizes between 2 mm and 25 mm and for antecedent dry-weather periods greater than 2 days. The total of 65.2% of rainfall retained over the monitoring period by the green roof alone increased to 75.6% when considering the total stormwater retained and detained together by the green roof and cistern, thus yielding a 10% improvement. The study results also demonstrate that the smart sensor’s use of weather forecasting data failed to improve system performance.

Air-sea interaction processes during hurricane Sandy: Coupled WRF-FVCOM model simulations

A fully-coupled atmospheric-ocean model was developed by coupling WRF (Weather Research and Forecasting Model) with FVCOM (the unstructured-grid, Finite-Volume Community Ocean Model) through the Earth System Model Framework (ESMF). The coupled WRF-FVCOM is configured with either hydrostatic or non-hydrostatic oceanic dynamics and can run with wave-current interactions. We applied this model to simulate the 2012 Hurricane Sandy in the western Atlantic Ocean. The experiments examined the impact of air-sea interactions on Sandy’s intensity/path and oceanic responses under hydrostatic and non-hydrostatic conditions. The results showed that the increased storm wind rapidly deepened the mixed layer depth when ocean processes were included. Intense vertical mixing brought cold water in the deep ocean towards the surface, producing a cold wake within the maximum wind zone underneath the storm. This process led to a sizeable latent heat loss from the ocean within the storm, and hence rapid air temperature and vapor mixing ratio drop above the sea surface. The storm intensified as the central sea-level pressure dropped. Improving air pressure simulation with ocean processes tended to reduce the storm size and strengthen its intensity, providing a better simulation of hurricane path and landfall. Turning on the non-hydrostatic process slightly improved the hurricane central sea-level pressure simulation and intensified the winds on the right side of the hurricane center. Hydrostatic and non-hydrostatic coupled WRF-FVCOMs captured Sandy-induced rapidly-varying flow over the shelf and the wind-induced surge level at the coast. The coupled models predicted a higher water elevation around the coastal areas where Sandy made landfall than the uncoupled model. The uncoupled and coupled models both showed more significant oceanic responses on the right side of the hurricane center, with a maximum during the Sandy crossing period when the clockwise-rotating frequency of Sandy wind was close to the local inertial frequency. The area with a maximum response varied with Sandy’s translation speed, more prominent in the deep region than over the slope, and more substantial under the non-hydrostatic condition. The simulated ocean responses agreed with the theoretical work of Price (1981). The nonhydrostatic experiments suggest that to resolve a fully storm-induced convection process, the oceanic model grid configuration should meet the O(1) criterion for the ratio of local water depth to the model horizontal resolution.

Optimizing the Assimilation of the GOES-16/-17 Atmospheric Motion Vectors in the Hurricane Weather Forecasting (HWRF) Model

Hourly and 15 min GOES-16 and -17 atmospheric motion vectors (AMVs) are evaluated using the 2020 version of the operational HWRF to assess their impact on tropical cyclone forecasting. The evaluation includes infrared (IR), visible (VIS), shortwave (SWIR), clear air, and cloud top water vapor (CAWV and CTWV) AMVs derived from the ABI imagery. Several changes are made to optimize the assimilation of these winds. The observational error profile is inflated to avoid overweighting of the AMVs. The range of allowable AMV wind speeds entering the assimilation system is increased to include larger wind speeds observed in tropical cyclones. Two data quality checks, commonly used for rejecting AMVs, namely QI and PCT1, have been removed. These changes resulted in a 20–40% increase in the number of AMVs assimilated. One additional change, specific to infrared AMVs, is narrowing the atmospheric layer where IR AMVs are rejected from 400–800 hPa to 400–600 hPa. The AMVs’ impact on forecast skill is assessed using storms from the North Atlantic and the Eastern Pacific, respectively. Overall, GOES-16 and -17 AMVs are beneficial for improving tropical cyclone forecasting. Positive analysis and forecast impact are obtained for track error, intensity error, minimum central pressure error, and storm size.

Robust Expansion of Extreme Midlatitude Storms Under Global Warming

AbstractExtreme extratropical storms are among the most impact‐relevant weather events in the midlatitudes. Under global warming, extreme storms are expected to intensify; however, little is known about the response of their spatial structure. Here, we show that with warming, extreme storms not only become stronger, but also grow larger. By employing multi‐model projections from the sixth Coupled Model Intercomparison Project and an idealized aquaplanet simulation, we demonstrate that global warming leads to a robust increase in extreme storm size that is relatively spatially uniform in the midlatitudes and consistent among the models. The physical mechanism for the size increase is the increase in the Rossby deformation radius due to the increase in low‐level dry static stability with warming. The storm expansion adds a substantial contribution (2.6%/K), along with the increase in precipitation intensity (3.2%/K), to the increase in storm total precipitation. The results improve our understanding and have significant implications for climate mitigation.

Accessing Insurance Flood Losses Using a Catastrophe Model and Climate Change Scenarios

Impact Forecasting has developed a catastrophe flood model for Czechia to estimate insurance losses. The model is built on a dataset of 12,066 years of daily rainfall and temperature data for the European area, representing the current climate (LAERTES-EU). This dataset was used as input to the rainfall–runoff model, resulting in a series of daily river channel discharges. Using analyses of global and regional climate models dealing with the impacts of climate change, this dataset was adjusted for the individual RCP climate scenarios in Europe. The river channel discharges were then re-derived using the already calibrated rainfall–runoff models. Based on the changed discharges, alternative versions of the standard catastrophe flood model for the Czechia were created for the various climate scenarios. In outputs, differences in severity, intensity, and number of events might be observed, as well as the size of storms. The effect on the losses might be investigated by probable maximum losses (PML) curves and average annual loss (AAL) values. For return period 1 in 5 years for the worst-case scenario, the differences can be up to +125 percent increase in insurance losses, while for the return period 1 in 100 years it is a −40 percent decrease. There is no significant effect of adaptation measures for the return period 1 in 100 years, but there is a −20 percent decrease in the return period 1 in 5 years.