Abstract

Visible satellite imagery is widely used by operational weather forecast centers for tropical and extratropical cyclone analysis and marine forecasting. The absence of visible imagery at night can significantly degrade forecast capabilities, such as determining tropical cyclone center locations or tracking warm-topped convective clusters. This paper documents ProxyVis imagery, an infrared-based proxy for daytime visible imagery developed to address the lack of visible satellite imagery at night and the limitations of existing nighttime visible options. ProxyVis was trained on the VIIRS day/night band imagery at times close to the full moon using VIIRS IR channels with closely matching GOES-16/17/18, Himawari-8/9, and Meteosat-9/10/11 channels. The final operational product applies the ProxyVis algorithms to geostationary satellite data and combines daytime visible and nighttime ProxyVis data to create full-disk animated GeoProxyVis imagery. The simple versions of the ProxyVis algorithm enable its generation from earlier GOES and Meteosat satellite imagery. ProxyVis offers significant improvement over existing operational products for tracking nighttime oceanic low-level clouds. Further, it is qualitatively similar to visible imagery for a wide range of backgrounds and synoptic conditions and phenomena, enabling forecasters to use it without special training. ProxyVis was first introduced to National Hurricane Center (NHC) operations in 2018 and was found to be extremely useful by forecasters becoming part of their standard operational satellite product suite in 2019. Currently, ProxyVis implemented for GOES-16/18, Himawari-9, and Meteosat-9/10/11 is being used in operational settings and evaluated for transition to operations at multiple NWS offices and the Joint Typhoon Warning Center.

Abstract

This study describes an automated analysis of real-time tropical cyclone (TC) aircraft reconnaissance observations to estimate TC surface winds. The wind analysis uses an iterative, objective, data-weighted analysis approach with different smoothing constraints in the radial and azimuthal directions. Smoothing constraints penalize the data misfit when the solutions deviate from smoothed analyses and extend the aircraft information into areas not directly observed. The analysis composites observations following storm motion taken within five hours prior and three hours after analysis time and makes use of prescribed methods to move observations to a Common Flight Level (CFL; 700-hPa) for analysis and reduce reconnaissance observations to the surface. Comparing analyses to several observed and simulated wind fields shows that analyses fit the observations while extending observational information to poorly observed regions. However, resulting analyses tend toward greater symmetry as observational coverage decreases, and show sensitivity to the first guess information in unobserved radii. Analyses produce reasonable and useful estimates of operationally important characteristics of the wind field. But, due to the radial and azimuthal smoothing and the under-sampling of typical aircraft reconnaissance flights, wind maxima are underestimated, and the radii of maximum wind are slightly overestimated. Varying observational coverage using model-based synthetic aircraft observations, these analyses improve as observational coverage increases, and for a typical observational pattern (two transects through the storm) the root-mean-square error deviation is < 10 kt (< 5 m s⁻¹).

Abstract

East African countries benefit economically from the largest freshwater lake in Africa: Lake Victoria (LV). Around 30 million people live along its coastline, and 5.4 million people subsist on its fishing industry. However, more than 1000 fishermen die annually by high-wave conditions often produced by severe convective wind phenomena, which marks this lake one of the deadliest places in the world for hazardous weather impacts. The World Meteorological Organization launched the 3-yr High Impact Weather Lake System (HIGHWAY) project, with the main objective to reduce loss of lives and economic goods in the lake basin and improve the resilience of the local communities. The project conducted a field campaign in 2019 aiming to provide forecasters with high-resolution observations and to study the storm life cycle over the lake basin. The research discussed here used the S-band polarimetric Tanzania radar from the field campaign to investigate the diurnal cycle of the convective mode over the lake. We classified the lake storms occurring during the two wet seasons into six different convective modes and present their diurnal evolution, organization, and main radar-based attributes, thereby extending the knowledge of convection on the lake. The result is the creation of a “convection catalog for Lake Victoria,” using the operational forecast lake sectors, and defining the exact times for the different timeslots resulting from the HIGHWAY project for the marine forecast. This will inform methods to improve the marine operational forecasts for Lake Victoria, and to provide the basis for new standard operation procedures (SOP) for severe weather surveillance and warning.

Abstract

Spatial patterns of tropical cyclone tornadoes (TCTs), and their relationship to patterns of mesoscale predictors within U.S. landfalling tropical cyclones (LTCs) are investigated using multicase composites from 27 years of reanalysis data (1995–2021). For 72 cases of LTCs with wide-ranging TC intensities at landfall, daytime TCT frequency maxima are found in the northeast, right-front, and downshear-right quadrants when their composites are constructed in ground-relative, TC-heading relative, and environmental shear relative coordinates, respectively. TCT maxima are located near maxima of 10-m–700-hPa bulk wind difference (BWD), which are enhanced by the TC circulation. This proxy for bulk vertical shear in roughly the lowest 3 km is among the best predictors of maximum TCT frequency. Relative to other times, the position of maximum TCT frequency during the afternoon shifts ∼100 km outward from the LTC center toward larger MLCAPE values. Composites containing the strongest LTCs have the strongest maximum 10-m–700-hPa and 10-m–500-hPa BWDs (∼20 m s⁻¹) with nearby maximum frequencies of TCTs. Corresponding composites containing weaker LTCs but still many TCTs, had bulk vertical shear values that were ∼20% smaller (∼16 m s⁻¹). Additional composites of cases having similarly weak average LTC strength at landfall, but few or no TCTs, had both maximum bulk vertical shears that were an additional ∼20% lower (∼12 m s⁻¹) and smaller MLCAPE. TCT environments occurring well inland are distinguished from others by having stronger westerly shear and a west–east-oriented baroclinic zone (i.e., north–south temperature gradient) that enhances mesoscale ascent and deep convection on the LTC’s east side.

Abstract

The primary source of guidance used by the Storm Surge Unit (SSU) at the National Hurricane Center (NHC) for issuing storm surge watches and warnings is the Probabilistic Tropical Storm Surge model (P-Surge). P-Surge is an ensemble of Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model forecasts that is generated based on historical error distributions from NHC official forecasts. A probabilistic framework is used for operational storm surge forecasting to account for uncertainty related to the tropical cyclone track and wind forcing. Previous studies have shown that the size of a storm’s wind field is an important factor that can affect storm surge. A simple radius of maximum wind (RMW) prediction scheme was developed to forecast RMW based on NHC forecast parameters. Verification results indicate this scheme is an improvement over the RMW forecasts used by previous versions of P-Surge. To test the impact of the updated RMW forecasts in P-Surge, retrospective cases were selected from 25 storms from 2008 to 2020 that had an adequate number of observations. Evaluation of P-Surge forecasts using these improved RMW forecasts shows that the probability of detection is higher for most probability of exceedance thresholds. In addition, the forecast reliability is improved, and there is an increase in the number of high probability forecasts for extreme events at longer lead times. The improved RMW forecasts were recently incorporated into the operational version of P-Surge (v2.9), and serve as an important step toward extending the lead time of skillful and reliable storm surge forecasts.

Abstract

A technique used widely to forecast the potential for QLCS mesovortices is known as the “Three Ingredients Method” (3IM). The 3IM states that mesovortices are favored where 1) the QLCS cold pool and ambient low-level shear are said to be nearly balanced or slightly shear dominant, 2) where the component of the 0–3-km wind shear normal to the convective line is ≥30 kt (1 kt ≈ 0.51 m s⁻¹), and 3) where a rear-inflow jet or enhanced outflow causes a surge or bow along the convective line. Despite its widespread use in operational settings, this method has received little evaluation in formal literature. To evaluate the 3IM, radiosonde observations are compared to radar-observed QLCS properties. The distance between the gust front and high reflectivity in the leading convective line (the “U-to-R distance”), the presence of rear-inflow surges, and mesovortices (MVs) were each assessed across 1820 line segments within 50 observed QLCSs. Although 0–3-km line-normal wind shear is statistically different between MV-genesis and null segments, values are ≤30 kt for 44% of MV-genesis segments. The 0–6-km line-normal wind shear also shows strong discrimination between MV-genesis and null segments and displays the best linear relationship of the U-to-R distance (a measure of system balance) among layers tested, although the scatter and overlap in distributions suggest that many factors can impact MV genesis (as expected). Overall, most MVs occur where the U-to-R distance lies between −5 and 5 km in the presence of a rear-inflow surge, along with positive 0–1-km wind shear, 0–3-km wind shear > 10 kt, and 0–6-km wind shear > 20 kt (all line-normal).

Abstract

On the local afternoon of 29 May 2012, a long-lived, right-moving (RM) supercell formed over northwestern Oklahoma and turned roughly southeastward. For >3 h, as it moved toward the Oklahoma City metro area, this supercell remained nontornadic and visually high-based, producing a nearly tornadic gustnado and a swath of significantly severe, sometimes giant hail up to 5 in (12.7 cm) in diameter. Meanwhile, a left-moving (LM) supercell formed over southwestern Oklahoma about 100 mi (161 km) south-southwest of the RM storm, and moved northeastward, with a rear-flank gust front that became well-defined on radar imagery as the LM storm approached southern and central parts of the metro. The authors, who had been observing the RM supercell in the field since genesis, surmised its potential future interaction with the LM storm’s trailing gust front about 1 h beforehand. We repositioned to near the gust front’s extrapolated collision point with the RM mesocyclone, in anticipation of maximized tornado potential, then witnessed a small tornado from the RM mesocyclone immediately following its interception of the boundary. Synchronized radar and photographic images of this remarkable sequence are presented and discussed in context of more recent findings on tornadic supercell/boundary interactions, with implications for operational utility.

Abstract

Heat is the leading cause of weather-related death in the United States. Wet bulb globe temperature (WBGT) is a heat stress index commonly used among active populations for activity modification, such as outdoor workers and athletes. Despite widespread use globally, WBGT forecasts have been uncommon in the United States until recent years. This research assesses the accuracy of WBGT forecasts developed by NOAA’s Southeast Regional Climate Center (SERCC) and the Carolinas Integrated Sciences and Assessments (CISA). It also details efforts to refine the forecast by accounting for the impact of surface roughness on wind using satellite imagery. Comparisons are made between the SERCC/CISA WBGT forecast and a WBGT forecast modeled after NWS methods. Additionally, both of these forecasts are compared with in situ WBGT measurements (during the summers of 2019-2021) and estimates from weather stations to assess forecast accuracy. The SERCC/CISA WBGT forecast was within 0.6°C of observations on average and showed less bias than the forecast based on NWS methods across North Carolina. Importantly, the SERCC/CISA WBGT forecast was more accurate for the most dangerous conditions (WBGT > 31°C), although this resulted in higher false alarms for these extreme conditions compared to the NWS method. In particular, this work improved the forecast for sites more sheltered from wind by better accounting for the influences of land cover on 2-meter wind speed. Accurate forecasts are more challenging for sites with complex microclimates. Thus, appropriate caution is necessary when interpreting forecasts and onsite, real-time WBGT measurements remain critical.

Abstract

The increased social need for more precise and reliable weather forecasts, especially when focusing on extreme weather events, pushes forward research and development in meteorology towards novel numerical weather prediction (NWP) systems that can provide simulations that resolve atmospheric processes on hectometric scales on demand. Such high-resolution NWP systems require a more detailed representation of the non-resolved processes, i.e. usage of scale-aware schemes for convection and three-dimensional turbulence (and radiation), which would additionally increase the computation needs. Therefore, developing and applying comprehensive, reliable, and computationally acceptable parametrizations in NWP systems is of urgent importance. All operationally used NWP systems are based on averaged Navier-Stokes equations, and thus require an approximation for the small-scale turbulent fluxes of momentum, energy, and matter in the system. The availability of high-fidelity data from turbulence experiments and direct numerical simulations has helped scientists in the past to construct and calibrate a range of turbulence closure approximations (from the relatively simple to more complex), some of which have been adopted and are in use in the current operational NWP systems. The significant development of learned-by-data (LBD) algorithms over the past decade (e.g. artificial intelligence) motivates engineers and researchers in fluid dynamics to explore alternatives for modeling turbulence by directly using turbulence data to quantify and reduce model uncertainties systematically. This review elaborates on the LBD approaches and their use in NWP currently, and also searches for novel data-informed turbulence models that can potentially be used and applied in NWP. Based on this literature analysis, the challenges and perspectives to do so are discussed.

Abstract

This study details a two-method, machine-learning approach to predict current and short-term intensity change in global tropical cyclones (TCs), ‘D-MINT’ and ‘D-PRINT’. D-MINT and D-PRINT use infrared imagery and environmental scalar predictors, while D-MINT also employs microwave imagery.

Results show that current TC intensity estimates from D-MINT and D-PRINT are more skillful than three established intensity estimation methods routinely used by operational forecasters for North Atlantic, and eastern and western North Pacific TCs.

Short-term intensity predictions are validated against five operational deterministic guidances at 6-, 12-, 18-, and 24-hour lead times. D-MINT and D-PRINT are less skillful than NHC and consensus TC intensity predictions in North Atlantic and eastern North Pacific TCs, but are more skillful than the other guidances for at least half of the lead times. In western North Pacific, North Indian Ocean, and Southern Hemisphere TCs, D-MINT is more skillful than the JTWC and other individual TC intensity forecasts for over half of the lead times. When probabilistically predicting TC rapid intensification (RI), D-MINT is more skillful in North Atlantic and western North Pacific TCs than three operationally-used RI guidances, but less skillful for yes-no RI forecasts.

In addition, this work demonstrates the importance of microwave imagery, as D-MINT is more skillful than D-PRINT. Since D-MINT and D-PRINT are convolutional neural network models interrogating two-dimensional structures within TC satellite imagery, this study also demonstrates that those features can yield better short-term predictions than existing scalar statistics of satellite imagery in operational models. Finally, a diagnostics tool is revealed to aid the attribution of the D-MINT/D-PRINT intensity predictions.