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Rebecca D. Adams-Selin
,
Christina Kalb
,
Tara Jensen
,
John Henderson
,
Tim Supinie
,
Lucas Harris
,
Yunheng Wang
,
Burkely T. Gallo
, and
Adam J. Clark

Abstract

Hail forecasts produced by the CAM-HAILCAST pseudo-Lagrangian hail size forecasting model were evaluated during the 2019, 2020, and 2021 NOAA HazardousWeather Testbed Spring Forecasting Experiments. As part of this evaluation, HWT SFE participants were polled about their definition of a “good” hail forecast. Participants were presented with two different verification methods conducted over three different spatiotemporal scales, and were then asked to subjectively evaluate the hail forecast as well as the different verificaiton methods themselves. Results recommended use of multiple verification methods tailored to the type of forecast expected by the end-user interpreting and applying the forecast.

The hail forecasts evaluated during this period included an implementation of CAM-HAILCAST in the Limited Area Model of the Unified Forecast System with the Finite Volume 3 (FV3) dynamical core. Evaluation of FV3-HAILCAST over both 1-h and 24-h periods found continued improvement from 2019 to 2021. The improvement was largely a result of wide intervariability among FV3 ensemble members with different microphysics parameterizations in 2019 lessening significantly during 2020 and 2021. Overprediction throughout the diurnal cycle also lessened by 2021. A combination of both upscaling neighborhood verification and an object-based technique that only retained matched convective objects was necessary to understand the improvement., agreeing with the HWT SFE participants’ recommendations for multiple verification methods.

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Xiaomin Chen
,
Andrew Hazelton
,
Frank D. Marks
,
Ghassan J. Alaka Jr
, and
Chunxi Zhang

Abstract

Continuous development and evaluation of planetary boundary layer (PBL) parameterizations in hurricane conditions are crucial for improving tropical cyclone (TC) forecasts. A turbulence kinetic energy (TKE)-based eddy-diffusivity mass-flux (EDMF-TKE) PBL scheme, implemented in NOAA’s Hurricane Analysis and Forecast System (HAFS), was recently improved in hurricane conditions using large-eddy simulations. This study evaluates the performance of HAFS TC forecasts with the original (experiment HAFA) and modified EDMF-TKE (experiment HAFY) based on a large sample of cases during the 2021 North Atlantic hurricane season. Results indicate that intensity and structure forecast skill was better overall in HAFY than in HAFA, including during rapid intensification. Composite analyses demonstrate that HAFY produces shallower and stronger boundary layer inflow, especially within 1–3 times the radius of maximum wind (RMW). Stronger inflow and more moisture in the boundary layer contribute to stronger moisture convergence near the RMW. These boundary layer characteristics are consistent with stronger, deeper, and more compact TC vortices in HAFY than in HAFA. Nevertheless, track skill in HAFY is slightly reduced, which is in part attributable to the cross-track error from a few early cycles of Hurricane Henri that exhibited ~400 n mi track error at longer lead times. Sensitivity experiments based on HAFY demonstrate that turning off cumulus schemes notably reduces the track errors of Henri while turning off the deep cumulus scheme reduces the intensity errors. This finding hints at the necessity of unifying the mass fluxes in PBL and cumulus schemes in future model physics development.

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Patrick Murphy
and
Clifford Mass

Abstract

This paper examines the relationship between daily carbon emissions for California’s savanna and forest wildfires and regional meteorology over the past 18 years. For each fuel type, the associated weather (daily maximum wind, daily vapor pressure deficient (VPD), and 30-day-prior VPD) is determined for all fire days, the first day of each fire, and the day of maximum emissions of each fire at each fire location. Carbon emissions, used as a marker of wildfire existence and growth, for both savanna and forest wildfires are found to vary greatly with regional meteorology, with the relationship between emissions and meteorology varying with the amount of emissions, fire location, and fuel type. Weak emissions are associated with climatologically typical dryness and wind. For moderate emissions, increasing emissions are associated with higher VPD from increased warming and only display a weak relationship with wind speed. High emissions, which encompass ~85% of the total emissions but only ~4% of the fire days, are associated with strong winds and large VPDs. Using spatial meteorological composites for California subregions, we find that weak-to-moderate emissions are associated with modestly warmer-than-normal temperatures and light winds across the domain. In contrast, high emissions are associated with strong winds and substantial temperature anomalies, with colder than normal temperatures east of the Sierra Nevada and warmer than normal conditions over the coastal zone and the interior of California.

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Aaron J. Hill
,
Russ S. Schumacher
, and
Israel Jirak

Abstract

Historical observations of severe weather and simulated severe weather environments (i.e., features) from the Global Ensemble Forecast System v12 (GEFSv12) Reforecast Dataset (GEFS/R) are used in conjunction to train and test random forest (RF) machine learning (ML) models to probabilistically forecast severe weather out to days 4–8. RFs are trained with ~9 years of the GEFS/R and severe weather reports to establish statistical relationships. Feature engineering is briefly explored to examine alternative methods for gathering features around observed events, including simplifying features using spatial averaging and increasing the GEFS/R ensemble size with time-lagging. Validated RF models are tested with ~1.5 years of real-time forecast output from the operational GEFSv12 ensemble and are evaluated alongside expert human-generated outlooks from the Storm Prediction Center (SPC). Both RF-based forecasts and SPC outlooks are skillful with respect to climatology at days 4 and 5 with diminishing skill thereafter. The RF-based forecasts exhibit tendencies to slightly underforecast severe weather events, but they tend to be well-calibrated at lower probability thresholds. Spatially averaging predictors during RF training allows for prior-day thermodynamic and kinematic environments to generate skillful forecasts, while time-lagging acts to expand the forecast areas, increasing resolution but decreasing overall skill. The results highlight the utility of ML-generated products to aid SPC forecast operations into the medium range.

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Christopher A. Kerr
,
Brian C. Matilla
,
Yaping Wang
,
Derek R. Stratman
,
Thomas A. Jones
, and
Nusrat Yussouf

Abstract

Since 2017, the Warn-on-Forecast System (WoFS) has been tested and evaluated during the Hazardous Weather Testbed Spring Forecasting Experiment (SFE) and summer convective seasons. The system has shown promise in predicting high temporal and spatial specificity of individual evolving thunderstorms. However, this baseline version of the WoFS has a 3-km horizontal grid spacing and cannot resolve some convective processes. Efforts are underway to develop a WoFS prototype at a 1-km grid spacing (WoFS-1km) with the hope to improve forecast accuracy. This requires extensive changes to data assimilation specifications and observation processing parameters. A preliminary version of WoFS-1km nested within WoFS at 3km (WoFS-3km) was developed, tested, and run during the 2021 SFE in pseudo-realtime. Ten case studies were successfully completed and provided simulations of a variety of convective modes.

The reflectivity and rotation storm objects from WoFS-1km are verified against both WoFS-3km and 1-km forecasts initialized from downscaled WoFS-3km analyses using both neighborhood- and object-based techniques. Neighborhood-based verification suggests WoFS-1km improves reflectivity bias but not spatial placement. The WoFS-1km object-based reflectivity forecast accuracy is higher in most cases, leading to a net improvement. Both the WoFS-1km and downscaled forecasts have ideal reflectivity object frequency biases while the WoFS-3km overpredicts the number of reflectivity objects. The rotation object verification is ambiguous as many cases are negatively impacted by 1-km data assimilation. This initial evaluation of a WoFS-1km prototype is a solid foundation for further development and future testing.

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Bu-Yo Kim
,
Miloslav Belorid
, and
Joo Wan Cha

Abstract

Accurate visibility prediction is imperative in the interests of human and environmental health. However, the existing numerical models for visibility prediction are characterized by low prediction accuracy and high computational cost. Thus, in this study, we predicted visibility using tree-based machine learning algorithms and numerical weather prediction data determined by the local data assimilation and prediction system (LDAPS) of the Korea Meteorological Administration. We then evaluated the accuracy of visibility prediction for Seoul, South Korea, through a comparative analysis using observed visibility from the automated synoptic observing system. The visibility predicted by machine learning algorithm was compared with the visibility predicted by LDAPS. The LDAPS data employed to construct the visibility prediction model were divided into learning, validation, and test sets. The optimal machine learning algorithm for visibility prediction was determined using the learning and validation sets. In this study, the extreme gradient boosting (XGB) algorithm showed the highest accuracy for visibility prediction. Comparative results using the test sets revealed lower prediction error and higher correlation coefficient for visibility predicted by the XGB algorithm (bias: −0.62 km, MAE: 2.04 km, RMSE: 2.94 km, and R: 0.88) than for that predicted by LDAPS (bias: −0.32 km, MAE: 4.66 km, RMSE: 6.48 km, and R: 0.40). Moreover, the mean equitable threat score (ETS) also indicated higher prediction accuracy for visibility predicted by the XGB algorithm (ETS: 0.5–0.6 for visibility ranges) than for that predicted by LDAPS (ETS: 0.1–0.2).

Open access
Lanxi Min
,
Qilong Min
, and
Yuyi Du

Abstract

Weather forecasting over complex terrain with diverse land cover is challenging. Utilizing the high-resolution observations from New York State Mesonet (NYSM), we are able to evaluate the surface processes of the Weather Research Forecast (WRF) Model in a detailed, scale-dependent manner. In the study, possible impacts of land–atmosphere interaction on surface meteorology and boundary layer cloud development are investigated with different model resolutions, land surface models (LSMs), and planetary boundary layer (PBL) physical parameterizations. The High-Resolution Rapid Refresh, version 3 (HRRR), forecasting model is used as a reference for the sensitivity evaluation. Results show that over complex terrain, the high-resolution simulations (1 km × 60 vertical levels) generally perform better compared to low-resolution (3 km × 50 levels) in both surface meteorology and cloud fields. LSMs play a more important role in surface meteorology compared to PBL schemes. The NoahMP land surface model exhibits daytime warmer and drier biases compared to the Rapid Update Cycle (RUC) due to better prediction of the Bowen ratio in RUC. The PBL schemes would affect the convective strength in the boundary layer. The Shin–Hong (SH) scale-aware scheme tends to produce the strongest convective strength in the PBL, while the ACM2 PBL scheme rarely resolved convection even at 1-km resolution. By considering the radiation effect of subgrid-scale (SGS) clouds, the Mellor–Yamada–Nakanishi–Niino eddy diffusivity mass flux (MYNN-EDMF) predicted the highest cloud coverage and lowest surface solar radiation bias. The configuration of SGS clouds in MYNN-EDMF would not only significantly reduce shortwave radiation bias, but also affect the convection behaviors through land surface–cloud–radiation interaction.

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Yung-Yun Cheng
,
Chia-Tung Chang
,
Buo-Fu Chen
,
Hung-Chi Kuo
, and
Cheng-Shang Lee

Abstract

This paper proposes a new quantitative precipitation estimation (QPE) technique to provide accurate rainfall estimates in complex terrain, where conventional QPE has limitations. The operational radar QPE in Taiwan is mainly based on the simplified relationship between radar reflectivity Z and rain rate R [R(Z) relation] and only utilizes the single-point lowest available echo to estimate rain rates, leading to low accuracy in complex terrain. Here, we conduct QPE using deep learning that extracts features from 3-D radar reflectivities to address the above issues. Convolutional neural networks (CNN) are used to analyze contoured frequency by altitude diagrams (CFADs) to generate the QPE. CNN models are trained on existing rain gauges in northern and eastern Taiwan with the three-year data during 2015–17 and validated and tested using 2018 data. The weights of heavy rains (≧10 mm h-1) are increased in the model loss calculation to handle the unbalanced rainfall data and improve accuracy.

Results show that the CNN outperforms the R(Z) relation based on the 2018 rain-gauge data. Furthermore, this research proposes methods to conduct 2-D gridded QPE at every pixel by blending estimates from various trained CNN models. Verification based on independent rain gauges shows that the CNN QPE solves the underestimation of the R(Z) relation in mountainous areas. Case studies are presented to visualize the results, showing that the CNN QPE generates better small-scale rainfall features and more accurate precipitation information. This deep learning QPE technique may be helpful for the disaster prevention of small-scale flash floods in complex terrain.

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Wei Ye
,
Ying Li
, and
Da-Lin Zhang

Abstract

In this study, the development of an extreme precipitation event along the southeastern margin of the Tibetan Plateau (TP) by the approach of Tropical Cyclone (TC) Rashmi (2008) from the Bay of Bengal is examined using a global reanalysis and all available observations. Results show the importance of an anomalous southerly flow, resulting from the merging of Rashmi into a meridionally deep trough at the western periphery of a subtropical high, in steering the storm and transporting tropical warm–moist air, thereby supplying necessary moisture for precipitation production over the TP. A mesoscale data analysis reveals that (i) the Rashmi vortex maintained its TC identity during its northward movement in the warm sector with weak-gradient flows; (ii) the extreme precipitation event occurred under potentially stable conditions; (iii) topographical uplifting of the southerly warm–moist air, enhanced by the approaching vortex with some degree of slantwise instability, led to the development of heavy to extreme precipitation along the southeastern margin of the TP; and (iv) the most influential uplifting of the intense vortex flows carrying ample moisture over steep topography favored the generation of the record-breaking daily snowfall of 98 mm (in water depth), and daily precipitation of 87 mm with rain–snow–rain changeovers at two high-elevation stations, respectively. The extreme precipitation and phase changeovers could be uncovered by an unusual upper-air sounding that shows a profound saturated layer from the surface to upper troposphere with a moist adiabatic upper 100-hPa layer and a bottom 100-hPa melting layer. The results appear to have important implications to the forecast of TC-related heavy precipitation over high mountains.

Significance Statement

This study attempts to gain insight into the multiscale dynamical processes leading to the development of an extreme precipitation event over the southeastern margin of the Tibet Plateau as a Bay of Bengal tropical cyclone (TC) approached. Results show (i) the importance of an anomalous southerly flow with a wide zonal span in steering the relatively large-sized TC and transporting necessary moisture into the region; and (ii) the subsequent uplifting of the warm and moist TC vortex by steep topography, producing the extreme precipitation event under potentially stable conditions, especially the record-breaking daily snowfall of 98 mm (in water depth). The results have important implications to the forecast of TC-related heavy precipitation over the Tibet Plateau and other high mountainous regions.

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Tseganeh Z. Gichamo
and
Clara S. Draper

Abstract

Within the National Weather Service’s Unified Forecast System (UFS), snow depth and snow cover observations are assimilated once daily using a rule-based method designed to correct for gross errors. While this approach improved the forecasts over its predecessors, it is now quite outdated and is likely to result in suboptimal analysis. We have then implemented and evaluated a snow data assimilation using the 2D optimal interpolation (OI) method, which accounts for model and observation errors and their spatial correlations as a function of distances between the observations and model grid cells. The performance of the OI was evaluated by assimilating daily snow depth observations from the Global Historical Climatology Network (GHCN) and the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover data into the UFS, from October 2019 to March 2020. Compared to the control analysis, which is very similar to the method currently in operational use, the OI improves the forecast snow depth and snow cover. For instance, the unbiased snow depth root-mean-squared error (ubRMSE) was reduced by 45 mm and the snow cover hit rate increased by 4%. This leads to modest improvements to globally averaged near-surface temperature (an average reduction of 0.23 K in temperature bias), with significant local improvements in some regions (much of Asia, the central United States). The reduction in near-surface temperature error was primarily caused by improved snow cover fraction from the data assimilation. Based on these results, the OI DA is currently being transitioned into operational use for the UFS.

Significance Statement

Weather and climate forecasting systems rely on accurate modeling of the evolution of atmospheric, oceanic, and land processes. In addition, model forecasts are substantially improved by continuous incorporation of observations to models, through a process called data assimilation. In this work, we upgraded the snow data assimilation used in the U.S. National Weather Service (NWS) global weather prediction system. Compared to the method currently in operational use, the new snow data assimilation improves both the forecasted snow quantity and near-surface air temperatures over snowy regions. Based on the positive results obtained in the experiments presented here, the new snow data assimilation method is being implemented in the NWS operational forecast system.

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