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Yanshuang Xie, Shaoping Shang, Jinquan Chen, Feng Zhang, Zhigan He, Guomei Wei, Jingyu Wu, Benlu Zhu, and Yindong Zeng

Abstract

Accurate storm surge forecasts provided rapidly could support timely decision-making with consideration of tropical cyclone (TC) forecasting error. This study developed a fast storm surge ensemble prediction method based on TC track probability forecasting and searching optimization of a numerical scenario database (SONSD). In a case study of the Fujian Province coast (China), a storm surge scenario database was established using numerical simulations generated by 93 150 hypothetical TCs. In a GIS-based visualization system, a single surge forecast representing 2562 distinct typhoon tracks and the occurrence probability of overflow of seawalls along the coast could be achieved in 1–2 min. Application to the cases of Typhoon Soudelor (2015) and Typhoon Maria (2018) demonstrated that the proposed method is feasible and effective. Storm surge calculated by SONSD had excellent agreement with numerical model results (i.e., mean MAE and RMSE: 7.1 and 10.7 cm, respectively, correlation coefficient: >0.9). Tide prediction also performed well with MAE/RMSE of 9.7/11.6 cm versus the harmonic tide, and MAE/RMSE of phase prediction for all high waters of 0.25/0.31 h versus observations. The predicted high-water level was satisfactory (MAE of 10.8 cm versus observations) when the forecasted and actual positions of the typhoon were close. When the forecasted typhoon position error was large, the ensemble surge prediction effectively reduced prediction error (i.e., the negative bias of −58.5 cm reduced to −5.2 cm versus observations), which helped avoid missed alert warnings. The proposed method could be applied in other regions to provide rapid and accurate decision-making support for government departments.

Open access
Aaron J. Hill and Russ S. Schumacher

Abstract

Approximately seven years of daily initializations from the convection-allowing National Severe Storms Laboratory Weather Research and Forecasting Model are used as inputs to train random forest (RF) machine learning models to probabilistically predict instances of excessive rainfall. Unlike other hazards, excessive rainfall does not have an accepted definition, so multiple definitions of excessive rainfall and flash flooding—including flash flood reports and 24-h average recurrence intervals (ARIs)—are used to explore RF configuration forecast sensitivities. RF forecasts are analogous to operational Weather Prediction Center (WPC) day-1 Excessive Rainfall Outlooks (EROs) and their resolution, reliability, and skill are strongly influenced by rainfall definitions and how inputs are assembled for training. Models trained with 1-yr ARI exceedances defined by the Stage-IV (ST4) precipitation analysis perform poorly in the northern Great Plains and Southwest United States, in part due to a high bias in the number of training events in these regions. Increasing the ARI threshold to 2 years or removing ST4 data from training, optimizing forecast skill geographically, and spatially averaging meteorological inputs for training generally results in improved CONUS-wide RF forecast skill. Both EROs and RF forecasts have seasonal skill—–poor forecasts in the late fall and winter and skillful forecasts in the summer and early fall. However, the EROs are consistently and significantly better than their RF counterparts, regardless of RF configuration, particularly in the summer months. The results suggest careful consideration should be made when developing ML-based probabilistic precipitation forecasts with convection-allowing model inputs, and further development is necessary to consider these forecast products for operational implementation.

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Jörg Steinert, Patrick Tracksdorf, and Dirk Heizenreder

Abstract

The analysis and forecast of precipitation characteristics is a key task for national meteorological services for providing high-quality weather forecasts and warnings. Beside the precipitation amount, the precipitation type is essential to describe and evaluate the recent, ongoing, and future weather situations. This paper introduces a new surface-based hybrid hydrometeor classification algorithm. The presented method combines polarimetric radar observations at radar beam height from the C-band dual-polarization weather radar network of the German Weather Service [Deutscher Wetterdienst (DWD)] with corrected thermodynamical profiles of numerical weather prediction (NWP) model output and extrapolates the hydrometeor classes at radar beam height to a height of 2 m above ground level (AGL). The implemented technique parameterizes the microphysical processes in the lower troposphere based on the appropriate thermodynamical profile that the hydrometeors have to pass along their way from the radar beam height to the surface. Due to errors in NWP output, the NWP vertical profiles of temperature and humidity are adjusted by using several types of surface stations with high spatial and temporal resolution. Verification results show considerable improvements in the hydrometeor classification near the ground compared to the radar sweep height. After an additional positive in-house evaluation, the presented method is integrated into DWD’s operational environment. The topic of this paper is to describe the processing steps for the computation of the near-surface precipitation type. In addition, example cases and a verification study complement the explanations.

Open access
Joseph B. Zambon, Ruoying He, John C. Warner, and Christie A. Hegermiller

Abstract

Hurricane Florence (2018) devastated the coastal communities of the Carolinas through heavy rainfall that resulted in massive flooding. Florence was characterized by an abrupt reduction in intensity (Saffir–Simpson category 4 to category 1) just prior to landfall and synoptic-scale interactions that stalled the storm over the Carolinas for several days. We conducted a series of numerical modeling experiments in coupled and uncoupled configurations to examine the impact of sea surface temperature (SST) and ocean waves on storm characteristics. In addition to experiments using a fully coupled atmosphere–ocean–wave model, we introduced the capability of the atmospheric model to modulate wind stress and surface fluxes by ocean waves through data from an uncoupled wave model. We examined these experiments by comparing track, intensity, strength, SST, storm structure, wave height, surface roughness, heat fluxes, and precipitation in order to determine the impacts of resolving ocean conditions with varying degrees of coupling. We found differences in the storm’s intensity and strength, with the best correlation coefficient of intensity (r = 0.89) and strength (r = 0.95) coming from the fully coupled simulations. Further analysis into surface roughness parameterizations added to the atmospheric model revealed differences in the spatial distribution and magnitude of the largest roughness lengths. Adding ocean and wave features to the model further modified the fluxes due to more realistic cooling beneath the storm, which in turn modified the precipitation field. Our experiments highlight significant differences in how air–sea processes impact hurricane modeling. The storm characteristics of track, intensity, strength, and precipitation at landfall are crucial to predictability and forecasting of future landfalling hurricanes.

Open access
Soo-Hyun Kim, Hye-Yeong Chun, Dan-Bi Lee, Jung-Hoon Kim, and Robert D. Sharman

Abstract

Based on a convective gravity wave drag parameterization scheme in a numerical weather prediction (NWP) model, previously proposed near-cloud turbulence (NCT) diagnostics for better detecting turbulence near convection are tested and evaluated by using global in situ flight data and outputs from the operational global NWP model of the Korea Meteorological Administration for one year (from December 2016 to November 2017). For comparison, 11 widely used clear air turbulence (CAT) diagnostics currently used in operational NWP-based aviation turbulence forecasting systems are separately computed. For selected cases, NCT diagnostics predict more accurately localized turbulence events over convective regions with better intensity, which is clearly distinguished from the turbulence areas diagnosed by conventional CAT diagnostics that they mostly failed to forecast with broad areas and low magnitudes. Although overall performance of NCT diagnostics for one whole year is lower than conventional CAT diagnostics due to the fact that NCT diagnostics exclusively focus on the isolated NCT events, adding the NCT diagnostics to CAT diagnostics improves the performance of aviation turbulence forecasting. Especially in the summertime, performance in terms of an area under the curve (AUC) based on probability of detection statistics is the best (AUC = 0.837 with a 4% increase, compared to conventional CAT forecasts) when the mean of all CAT and NCT diagnostics is used, while performance in terms of root-mean-square error is the best when the maximum among combined CAT and single NCT diagnostic is used. This implies that including NCT diagnostics to currently used NWP-based aviation turbulence forecasting systems should be beneficial for safety of air travel.

Restricted access
Makenzie J. Krocak and Harold E. Brooks

Abstract

While many studies have looked at the quality of forecast products, few have attempted to understand the relationship between them. We begin to consider whether or not such an influence exists by analyzing storm-based tornado warning product metrics with respect to whether they occurred within a severe weather watch and, if so, what type of watch they occurred within. The probability of detection, false alarm ratio, and lead time all show a general improvement with increasing watch severity. In fact, the probability of detection increased more as a function of watch-type severity than the change in probability of detection during the time period of analysis. False alarm ratio decreased as watch type increased in severity, but with a much smaller magnitude than the difference in probability of detection. Lead time also improved with an increase in watch-type severity. Warnings outside of any watch had a mean lead time of 5.5 min, while those inside of a particularly dangerous situation tornado watch had a mean lead time of 15.1 min. These results indicate that the existence and type of severe weather watch may have an influence on the quality of tornado warnings. However, it is impossible to separate the influence of weather watches from possible differences in warning strategy or differences in environmental characteristics that make it more or less challenging to warn for tornadoes. Future studies should attempt to disentangle these numerous influences to assess how much influence intermediate products have on downstream products.

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Free access
Clifford F. Mass, David Ovens, Robert Conrick, and John Saltenberger

Abstract

A series of major fires spread across eastern Washington and western Oregon starting on 7 September 2020, driven by strong easterly and northeasterly winds gusting to ~70 kt (1 kt ≈ 0.51 m s−1) at exposed locations. This event was associated with a high-amplitude upper-level ridge over the eastern Pacific and a mobile trough that moved southward on its eastern flank. The synoptic environment during the event was highly unusual, with the easterly 925-hPa wind speeds at Salem, Oregon, being unprecedented for the August–September period. The September 2020 wildfires produced dense smoke that initially moved westward over the Willamette Valley and eventually covered the region. As a result, air quality rapidly degraded to hazardous levels, representing the worst air quality period of recent decades. High-resolution numerical simulations using the WRF Model indicated the importance of a high-amplitude mountain wave in producing strong easterly winds over western Oregon. The dead fuel moisture levels over eastern Washington before the fires were typical for that time of the year. Along the western slopes of the Oregon Cascades, where the fuels largely comprise a dense conifer forest with understory vegetation, fire weather indices were lower (moister) than normal during the early part of the summer, but transitioned to above-normal (drier) values during August, with a spike to record values in early September coincident with the strong easterly winds. Forecast guidance was highly accurate for both the Washington and Oregon wildfire events. Analyses of climatological data and fuel indices did not suggest that unusual preexisting climatic conditions were major drivers of the September 2020 Northwest wildfires.

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Edward J. Strobach

Abstract

Parameterizing boundary layer turbulence is a critical component of numerical weather prediction and the representation of turbulent mixing of momentum, heat, and other tracers. The components that make up a boundary layer scheme can vary considerably, with each scheme having a combination of processes that are physically represented along with tuning parameters that optimize performance. Isolating a component of a PBL scheme to examine its impact is essential for understanding the evolution of boundary layer profiles and their impact on the mean structure. In this study we conduct three experiments with the scale-aware TKE eddy-diffusivity mass-flux (sa-TKE-EDMF) scheme: 1) releasing the upper limit constraints placed on mixing lengths, 2) incrementally adjusting the tuning coefficient related to wind shear in the modified Bougeault and Lacarrere (BouLac) mixing length formulation, and 3) replacing the current mixing length formulations with those used in the MYNN scheme. A diagnostic approach is adopted to characterize the bulk representation of turbulence within the residual layer and boundary layer in order to understand the importance of different terms in the TKE budget as well as to assess how the balance of terms changes between mixing length formulations. Although our study does not seek to determine the best formulation, it was found that strong imbalances led to considerably different profile structures both in terms of the resolved and subgrid fields. Experiments where this balance was preserved showed a minor impact on the mean structure regardless of the turbulence generated. Overall, it was found that changes to mixing length formulations and/or constraints had stronger impacts during the day while remaining partially insensitive during the evening.

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Franziska Hellmuth, Bjørg Jenny Kokkvoll Engdahl, Trude Storelvmo, Robert O. David, and Steven J. Cooper

Abstract

In the winter, orographic precipitation falls as snow in the mid- to high latitudes where it causes avalanches, affects local infrastructure, or leads to flooding during the spring thaw. We present a technique to validate operational numerical weather prediction model simulations in complex terrain. The presented verification technique uses a combined retrieval approach to obtain surface snowfall accumulation and vertical profiles of snow water at the Haukeliseter test site, Norway. Both surface observations and vertical profiles of snow are used to validate model simulations from the Norwegian Meteorological Institute’s operational forecast system and two simulations with adjusted cloud microphysics. Retrieved surface snowfall is validated against measurements conducted with a double-fence automated reference gauge (DFAR). In comparison, the optimal estimation snowfall retrieval produces +10.9% more surface snowfall than the DFAR. The predicted surface snowfall from the operational forecast model and two additional simulations with microphysical adjustments (CTRL and ICE-T) are overestimated at the surface with +41.0%, +43.8%, and +59.2%, respectively. Simultaneously, the CTRL and ICE-T simulations underestimate the mean snow water path by −1071.4% and −523.7%, respectively. The study shows that we would reach false conclusions only using surface accumulation or vertical snow water content profiles. These results highlight the need to combine ground-based in situ and vertically profiling remote sensing instruments to identify biases in numerical weather prediction.

Open access