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Cameron J. Nixon and John T. Allen

Abstract

Hodographs are valuable sources of pattern recognition in severe convective storm forecasting. Certain shapes are known to discriminate between single cell, multicell, and supercell storm organization. Various derived quantities such as storm-relative helicity (SRH) have been found to predict tornado potential and intensity. Over the years, collective research has established a conceptual model for tornadic hodographs (large and “looping,” with high SRH). However, considerably less attention has been given to constructing a similar conceptual model for hodographs of severe hail. This study explores how hodograph shape may differentiate between the environments of severe hail and tornadoes. While supercells are routinely assumed to carry the potential to produce all hazards, this is not always the case, and we explore why. The Storm Prediction Center (SPC) storm mode dataset is used to assess the environments of 8958 tornadoes and 7256 severe hail reports, produced by right- and left-moving supercells. Composite hodographs and indices to quantify wind shear are assessed for each hazard, and clear differences are found between the kinematic environments of hail-producing and tornadic supercells. The sensitivity of the hodograph to common thermodynamic variables was also examined, with buoyancy and moisture found to influence the shape associated with the hazards. The results suggest that differentiating between tornadic and hail-producing storms may be possible using properties of the hodograph alone. While anticipating hail size does not appear possible using only the hodograph, anticipating tornado intensity appears readily so. When coupled with buoyancy profiles, the hodograph may assist in differentiating between both hail size and tornado intensity.

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Stephen J. Lord, Xingren Wu, Vijay Tallapragada, and F. M. Ralph

The impact of assimilating dropsonde data from the 2020 Atmospheric River (AR) Reconnaissance (ARR) field campaign on operational numerical precipitation forecasts was assessed. Two experiments were executed for the period from 24 January to 18 March 2020 using the NCEP Global Forecast System version 15 (GFSv15) with a four-dimensional hybrid ensemble-variational (4DEnVar) data assimilation system. The control run (CTRL) used all the routinely assimilated data and included ARR dropsonde data, whereas the denial run (DENY) excluded the dropsonde data. There were 17 Intensive Observing Periods (IOPs) totaling 46 Air Force C-130 and 16 NOAA G-IV missions to deploy dropsondes over targeted regions with potential for downstream high-impact weather associated with the ARs. Data from a total of 628 dropsondes were assimilated in the CTRL. The dropsonde data impact on precipitation forecasts over U.S. West Coast domains is largely positive, especially for day 5 lead time, and appears driven by different model variables on a case-by-case basis. These results suggest that data gaps associated with ARs can be addressed with targeted ARR field campaigns providing vital observations needed for improving U.S. West Coast precipitation forecasts.

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Adam J. Clark and Eric D. Loken

Abstract

Severe weather probabilities are derived from the Warn-on-Forecast System (WoFS) run by NOAA’s National Severe Storms Laboratory (NSSL) during spring 2018 using the random forest (RF) machine learning algorithm. Recent work has shown this method generates skillful and reliable forecasts when applied to convection-allowing model ensembles for the “Day 1” time range (i.e., 12–36-h lead times), but it has been tested in only one other study for lead times relevant to WoFS (e.g., 0–6 h). Thus, in this paper, various sets of WoFS predictors, which include both environment and storm-based fields, are input into a RF algorithm and trained using the occurrence of severe weather reports within 39 km of a point to produce severe weather probabilities at 0–3-h lead times. We analyze the skill and reliability of these forecasts, sensitivity to different sets of predictors, and avenues for further improvements. The RF algorithm produced very skillful and reliable severe weather probabilities and significantly outperformed baseline probabilities calculated by finding the best performing updraft helicity (UH) threshold and smoothing parameter. Experiments where different sets of predictors were used to derive RF probabilities revealed 1) storm attribute fields contributed significantly more skill than environmental fields, 2) 2–5 km AGL UH and maximum updraft speed were the best performing storm attribute fields, 3) the most skillful ensemble summary metric was a smoothed mean, and 4) the most skillful forecasts were obtained when smoothed UH from individual ensemble members were used as predictors.

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Clifford Mass, Calen Randall, Robert Conrick, and David Ovens

Abstract

The development of sea surface temperature (SST) anomalies over the northeast Pacific and their impacts on lower-tropospheric air temperatures over the Pacific Northwest are examined. Northeast Pacific SST anomalies are influenced by the synoptic-scale flow, with high pressure and weak surface winds associated with developing warm SST anomalies, while large pressure gradients and strong surface winds result in SST declines. SST over the northeast Pacific correlates significantly with surface air temperatures over the Pacific Northwest, with correlations increasing when high-frequency variability is filtered out. The correlations between unfiltered time series of SST and surface air temperature are largest for a zero-day lag and are strongest near the coast, contrasting with weaker correlations over the Columbia basin east of the Cascade Mountains. SST correlations with minimum surface air temperature are largest during the warm season, and maximum temperature correlations are highest in March; both have low correlations during autumn. Model simulations of periods with warm and cold northeast Pacific SST anomalies possess lower-tropospheric air temperature warming or cooling over the coastal zone, with SST influence weakening east of the Cascade crest. Eastern Pacific SST anomalies influence sea level pressure and lower-tropospheric heights, with warm SST anomalies resulting in simulated lowered pressure near the surface and increased heights aloft. The relationship between northeast Pacific SST and surface air temperature over land evince complex feedbacks: SST temperature anomalies can be advected inland from the Pacific, the SST anomalies can influence the synoptic-scale flow that affects the SST anomalies, and the synoptic-scale anomalies that produce the SST anomalies can directly influence temperatures over land.

Significance Statement

Understanding the connection between northeast Pacific sea surface temperatures and low-level air temperatures over land is valuable for both subseasonal prediction and for examining the fidelity of model physics.

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M.M. Nageswararao, Yuejian Zhu, Vijay Tallapragada, and Meng-Shih Chen

The skillful prediction of monthly scale rainfall at small regions like Taiwan is one of the challenges of the meteorological scientific community. Taiwan is one of the sub-tropical islands in Asia. It experiences rainfall extremes regularly, leading to landslides and flash floods in/near the mountains and flooding over low-lying plains, particularly during the summer monsoon season (June through September; JJAS). In September 2020, NOAA NCEP implemented Global Ensemble Forecast System version 12 (GEFSv12) to support stakeholders for sub-seasonal forecasts and hydrological applications. In the present study, the performance evaluation of GEFSv12 for monthly rainfall and associated extreme rainfall (ER) events over Taiwan during JJAS against CMORPH has been done. There is a marginal improvement of GEFSv12 in depicting the East Asian Summer Monsoon Index (EASMI) as compared to GEFS-SubX. The GEFSv12 rainfall raw products have been calibrated with a quantile-quantile (QQ) mapping technique for further prediction skill improvement. The results reveal that the spatial patterns of climatological features (mean, inter-annual variability, and coefficient of variation) of summer monsoon monthly rainfall over Taiwan from QQ-GEFSv12 are very similar to CMORPH than Raw-GEFSv12. Raw-GEFSv12 has an enormous wet bias and over-forecast Wet days, while QQ-GEFSv12 is close to reality. The prediction skill (correlation coefficient and Index of Agreement) of GEFSv12 in depicting the summer monsoon monthly rainfall over Taiwan is significantly high (>0.5) in most parts of Taiwan and particularly more during peak monsoon months, September, and August, followed by June and July. The calibration method significantly reduces the overestimation (underestimation) of Wet (ER) events from the ensemble mean and probabilistic ensemble forecasts. The predictability of extreme rainfall events (>50mm/day) has also improved significantly.

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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-elevated 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.

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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 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 resolution, Land Surface Model (LSM), and Planetary Boundary Layer (PBL) physical parameterizations. The High Resolution Rapid Refresh version 3 (HRRR) forecasting is used as a reference for the sensitivity evaluation. Results shows that over the complex terrain, the high-resolution simulations (1-km × 60 vertical levels) generally performs better compared to low-resolution (3km × 50 levels) in both surface meteorology and cloud fields. LSMs play a more important role in surface meteorology compared to PBL schemes. NoahMP land surface model exhibits daytime warmer and drier biases compared to Rapid Update Cycle (RUC) due to better prediction of Bowen Ratio in RUC. The PBL schemes would affect the convective strength in the boundary layer. Shin-Hong (SH) scale-aware scheme tends to produce strongest convective strength in PBL, while ACM2 PBL scheme rarely resolved convection even at 1-km resolution. By considering the radiation effect of Subgrid Scale (SGS) clouds, Mellor-Yamada-Nakanishi-Niino Eddy Diffusivity-Mass Flux (MYNN-EDMF) predict 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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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 Multi-sensor Snow and Ice Mapping System (IMS) snow cover data into the UFS, from October 2019 – 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 45mm and the snow cover hit rate increased by 4%. This leads to modest improvements to globally averaged near surface temperature (an average of 0.23K reduction in temperature bias), with significant local improvements in some regions (much of Asia, the central US). 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.

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Chung-Chieh Wang, Hung-Chi Kuo, Yu-Han Chen, Shin-Hau Chen, and Kazuhisa Tsuboki

Typhoon Morakot struck Taiwan during 7-9 August 2009 and became the deadliest tropical cyclone (TC) in five decades by producing up to 2635 mm of rain in 48 h, breaking the world record. The extreme rainfall of Morakot resulted from the strong interaction among several favorable factors that occurred simultaneously. These factors from large scale to small scale include: (1) weak environmental steering flow linked to the evolution of the monsoon gyre and consequently slow TC motion; (2) a strong moisture surge due to low-level southwesterly flow; (3) asymmetric rainfall and latent heating near southern Taiwan to further reduce the TC’s forward motion as its center began moving away from Taiwan; (4) enhanced rainfall due to steep topography; (5) atypical structure with a weak inner core, enhancing its susceptibility to the latent heating effect; and (6) cell merger and back building inside the rainbands associated with the interaction between the low-level jet and convective updrafts.

From a forecasting standpoint, the present-day convective-permitting or cloud-resolving regional models are capable of short-range predictions of the Morakot event starting from 6 August. At longer ranges beyond 3 days, larger uncertainty exists in the track forecast and an ensemble approach is necessary. Due to the large computational demand at the required high resolution, the time-lagged strategy is shown to be a feasible option to produce useful information on rainfall probabilities of the event.

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