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Evan S. Bentley, Richard L. Thompson, Barry R. Bowers, Justin G. Gibbs, and Steven E. Nelson

Abstract

Previous work has considered tornado occurrence with respect to radar data, both WSR-88D and mobile research radars, and a few studies have examined techniques to potentially improve tornado warning performance. To date, though, there has been little work focusing on systematic, large-sample evaluation of National Weather Service (NWS) tornado warnings with respect to radar-observable quantities and the near-storm environment. In this work, three full years (2016–18) of NWS tornado warnings across the contiguous United States were examined, in conjunction with supporting data in the few minutes preceding warning issuance, or tornado formation in the case of missed events. The investigation herein examines WSR-88D and Storm Prediction Center (SPC) mesoanalysis data associated with these tornado warnings with comparisons made to the current Warning Decision Training Division (WDTD) guidance. Combining low-level rotational velocity and the significant tornado parameter (STP), as used in prior work, shows promise as a means to estimate tornado warning performance, as well as relative changes in performance as criteria thresholds vary. For example, low-level rotational velocity peaking in excess of 30 kt (15 m s−1), in a near-storm environment, which is not prohibitive for tornadoes (STP > 0), results in an increased probability of detection and reduced false alarms compared to observed NWS tornado warning metrics. Tornado warning false alarms can also be reduced through limiting warnings with weak (<30 kt), broad (>1 n mi; 1 n mi = 1.852 km) circulations in a poor (STP = 0) environment, careful elimination of velocity data artifacts like sidelobe contamination, and through greater scrutiny of human-based tornado reports in otherwise questionable scenarios.

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Juxiang Peng, Yuanfu Xie, and Zhaoping Kang

Abstract

This paper reports the assimilation of cloud optical depth datasets into a variational data assimilation system to improve cloud ice, cloud water, rain, snow, and graupel analysis in extreme weather events for improving forecasts. A cloud optical depth forward operator was developed and implemented in the Space and Time Multiscale Analysis System (STMAS), a multiscale three-dimensional variational analysis system. Using this improved analysis system, the NOAA GOES-15 Daytime Cloud Optical and Microphysical Properties (DCOMP) cloud optical depth products were assimilated to improve the microphysical states. For an 8-day period of extreme weather events in September 2013 in Colorado, the United States, the impact of the cloud optical depth assimilation on the analysis results and forecasts was evaluated. The DCOMP products improved the cloud ice and cloud water predictions significantly in convective and lower levels. The DCOMP products also reduced errors in temperature and relative humidity data at the top (250–150 hPa) and bottom (850–700 hPa) layers. With the cloud ice improvement at higher layers, the DCOMP products provided better forecasts of cloud liquid at low layers (900–700 hPa), temperature and wind at all layers, and relative humidity at middle and bottom layers. Furthermore, for this extreme weather event, both equitable threat score (ETS) and bias were improved throughout the 12-h period, with the most significant improvement observed in the first 3 h. This study will raise the expectation of cloud optical depth product assimilation in operational applications.

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Diego Pons, Ángel G. Muñoz, Ligia M. Meléndez, Mario Chocooj, Rosario Gómez, Xandre Chourio, and Carmen González Romero

Abstract

The provision of climate services has the potential to generate adaptive capacity and help coffee farmers become or remain profitable by integrating climate information in a risk-management framework. Yet, to achieve this goal, it is necessary to identify the local demand for climate information, the relationships between coffee yield and climate variables, and farmers’ perceptions and to examine the potential actions that can be realistically put in place by farmers at the local level. In this study, we assessed the climate information demands from coffee farmers and their perception on the climate impacts to coffee yield in the Samalá watershed in Guatemala. After co-identifying the related candidate climate predictors, we propose an objective, flexible forecast system for coffee yield that is based on precipitation. The system, known as NextGen, analyzes multiple historical climate drivers to identify candidate predictors and provides both deterministic and probabilistic forecasts for the target season. To illustrate the approach, a NextGen implementation is conducted in the Samalá watershed in southwestern Guatemala. The results suggest that accumulated June–August precipitation provides the highest predictive skill associated with coffee yield for this region. In addition to a formal cross-validated skill assessment, retrospective forecasts for the period 1989–2009 were compared with agriculturalists’ perception on the climate impacts to coffee yield at the farm level. We conclude with examples of how demand-based climate service provision in this location can inform adaptation strategies like optimum shade, pest control, and fertilization schemes months in advance. These potential adaptation strategies were validated by local agricultural technicians at the study site.

Open access
Robert M. Banta, Yelena L. Pichugina, Lisa S. Darby, W. Alan Brewer, Joseph B. Olson, Jaymes S. Kenyon, S. Baidar, S. G. Benjamin, H. J. S. Fernando, K. O. Lantz, J. K. Lundquist, B. J. McCarty, T. Marke, S. P. Sandberg, J. Sharp, W. J. Shaw, D. D. Turner, J. M. Wilczak, R. Worsnop, and M. T. Stoelinga

Abstract

Complex-terrain locations often have repeatable near-surface wind patterns, such as synoptic gap flows and local thermally forced flows. An example is the Columbia River Valley in east-central Oregon–Washington, a significant wind energy generation region and the site of the Second Wind Forecast Improvement Project (WFIP2). Data from three Doppler lidars deployed during WFIP2 define and characterize summertime wind regimes and their large-scale contexts, and provide insight into NWP model errors by examining differences in the ability of a model [NOAA’s High-Resolution Rapid Refresh (HRRR version 1)] to forecast wind speed profiles for different flow regimes. Seven regimes were identified based on daily time series of the lidar-measured rotor-layer winds, which then suggested two broad categories. First, in three of the regimes the primary dynamic forcing was the large-scale pressure gradient. Second, in two other regimes the dominant forcing was the diurnal heating-cooling cycle (regional sea-breeze-type dynamics), including the marine intrusion previously described, which generates strong nocturnal winds over the region. For the large-scale pressure gradient regimes, HRRR had wind speed biases of ~1 m s−1 and RMSEs of 2–3 m s−1. Errors were much larger for the thermally forced regimes, owing to the premature demise of the strong nocturnal flow in HRRR. Thus, the more dominant the role of surface heating in generating the flow, the larger the errors. Major errors could result from surface heating of the atmosphere, boundary layer responses to that heating, and associated terrain interactions. Measurement/modeling research programs should be designed to determine which of these modeled processes produce the largest errors, so those processes can be improved and errors reduced.

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Casey E. Davenport

Abstract

Long-lived supercells (containing mesocyclones persisting for at least 4 h) are relatively rare, but present significant risk for society as a result of their intensity and associated hazards over an extended time period. The persistence of a rotating updraft is tied to near-storm environmental characteristics; however, given the established prevalence of mesoscale environmental heterogeneity near severe convection, it is unknown to what extent those near-storm characteristics vary over the lifetime of a supercell, nor how quickly the storm responds to such changes. This study examines 147 long-lived, isolated supercells, focusing on the evolution of their near-storm environments using model analysis soundings generated each hour throughout the storm’s lifetime. Environmental variability is quantified via a series of common forecasting parameters, with impacts of measured changes related to production of severe weather and overall storm longevity. The diurnal and maturity-relative distributions of forecasting parameters are examined, along with comparisons among subsets of marginally versus very long-lived supercells, as well as dissipation before versus after sunset. The diurnal cycle is a dominant trend over the lifetime of all supercells, with attendant impacts to relevant thermodynamic and kinematic parameters, timing of storm initiation and dissipation, as well as severe weather production. Notably, changes in the near-storm environment are connected to supercell longevity and generation of severe weather reports. The long-term goal of the above analyses is to enhance short-term forecasts of supercells by better anticipating storm evolution as a result of environmental variations.

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Christoph Mony, Lukas Jansing, and Michael Sprenger

Abstract

This study explores the possibilities of employing machine learning algorithms to predict foehn occurrence in Switzerland at a north Alpine (Altdorf) and south Alpine (Lugano) station from its synoptic fingerprint in reanalysis data and climate simulations. This allows for an investigation on a potential future shift in monthly foehn frequencies. First, inputs from various atmospheric fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERAI) were used to train an XGBoost model. Here, similar predictive performance to previous work was achieved, showing that foehn can accurately be diagnosed from the coarse synoptic situation. In the next step, the algorithm was generalized to predict foehn based on the Community Earth System Model (CESM) ensemble simulations of a present-day and warming future climate. The best generalization between ERAI and CESM was obtained by including the present-day data in the training procedure and simultaneously optimizing two objective functions, namely, the negative log loss and squared mean loss, on both datasets, respectively. It is demonstrated that the same synoptic fingerprint can be identified in CESM climate simulation data. Finally, predictions for present-day and future simulations were verified and compared for statistical significance. Our model is shown to produce valid output for most months, revealing that south foehn in Altdorf is expected to become more common during spring, while north foehn in Lugano is expected to become more common during summer.

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Jason M. English, David D. Turner, Trevor I. Alcott, William R. Moninger, Janice L. Bytheway, Robert Cifelli, and Melinda Marquis

Abstract

Improved forecasts of atmospheric river (AR) events, which provide up to half the annual precipitation in California, may reduce impacts to water supply, lives, and property. We evaluate quantitative precipitation forecasts (QPF) from the High-Resolution Rapid Refresh model version 3 (HRRRv3) and version 4 (HRRRv4) for five AR events that occurred in February–March 2019 and compare them to quantitative precipitation estimates (QPE) from Stage IV and Mesonet products. Both HRRR versions forecast spatial patterns of precipitation reasonably well, but are drier than QPE products in the Bay Area and wetter in the Sierra Nevada range. The HRRR dry bias in the Bay Area may be related to biases in the model temperature profile, while integrated water vapor (IWV), wind speed, and wind direction compare reasonably well. In the Sierra Nevada range, QPE and QPF agree well at temperatures above freezing. Below freezing, the discrepancies are due in part to errors in the QPE products, which are known to underestimate frozen precipitation in mountainous terrain. HRRR frozen QPF accuracy is difficult to quantify, but the model does have wind speed and wind direction biases near the Sierra Nevada range. HRRRv4 is overall more accurate than HRRRv3, likely due to data assimilation improvements, and possibly physics improvements. Applying a neighborhood maximum method impacted performance metrics, but did not alter general conclusions, suggesting closest gridbox evaluations may be adequate for these types of events. Improvements to QPF in the Bay Area and QPE/QPF in the Sierra Nevada range would be particularly useful to provide better understanding of AR events.

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Luke J. LeBel, Brian H. Tang, and Ross A. Lazear

Abstract

The complex terrain at the intersection of the Mohawk and Hudson valleys of New York has an impact on the development and evolution of severe convection in the region. Specifically, previous research has concluded that terrain-channeled flow in the Mohawk and Hudson valleys likely contributes to increased low-level wind shear and instability in the valleys during severe weather events such as the historic 31 May 1998 event that produced a strong (F3) tornado in Mechanicville, New York. The goal of this study is to further examine the impact of terrain channeling on severe convection by analyzing a high-resolution WRF Model simulation of the 31 May 1998 event. Results from the simulation suggest that terrain-channeled flow resulted in the localized formation of an enhanced low-level moisture gradient, resembling a dryline, at the intersection of the Mohawk and Hudson valleys. East of this boundary, the environment was characterized by stronger low-level wind shear and greater low-level moisture and instability, increasing tornadogenesis potential. A simulated supercell intensified after crossing the boundary, as the larger instability and streamwise vorticity of the low-level inflow was ingested into the supercell updraft. These results suggest that terrain can have a key role in producing mesoscale inhomogeneities that impact the evolution of severe convection. Recognition of these terrain-induced boundaries may help in anticipating where the risk of severe weather may be locally enhanced.

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Timothy Olander, Anthony Wimmers, Christopher Velden, and James P. Kossin

Abstract

Several simple and computationally inexpensive machine learning models are explored that can use advanced Dvorak technique (ADT)-retrieved features of tropical cyclones (TCs) from satellite imagery to provide improved maximum sustained surface wind speed (MSW) estimates. ADT (version 9.0) TC analysis parameters and operational TC forecast center best track datasets from 2005 to 2016 are used to train and validate the various models over all TC basins globally and select the best among them. Two independent test sets of TC cases from 2017 to 2018 are used to evaluate the intensity estimates produced by the final selected model called the “artificial intelligence (AI)” enhanced advanced Dvorak technique (AiDT). The 2017–18 MSW results demonstrate a global RMSE of 7.7 and 8.2 kt (1 kt ≈ 0.51 m s−1), respectively. Basin-specific MSW RMSEs of 8.4, 6.8, 7.3, 8.0, and 7.5 kt were obtained with the 2017 dataset in the North Atlantic, east/central Pacific, northwest Pacific, South Pacific/south Indian, and north Indian Ocean basins, respectively, with MSW RMSE values of 8.9, 6.7, 7.1, 10.4, and 7.7 obtained with the 2018 dataset. These represent a 30% and 23% improvement over the corresponding ADT RMSE for the 2017–18 datasets, respectively, with the AiDT error reduction significant to 99% in both sets. The AiDT model represents a notable improvement over the ADT performance and also compares favorably to more computationally expensive and complex machine learning models that interrogate satellite images directly while still preserving the operational familiarity of the ADT.

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Erin E. Thomas, Malte Müller, Patrik Bohlinger, Yurii Batrak, and Nicholas Szapiro

Abstract

Accurately simulating the interactions between the components of a coupled Earth modeling system (atmosphere, sea ice, and wave) on a kilometer-scale resolution is a new challenge in operational numerical weather prediction. It is difficult due to the complexity of interactive mechanisms, the limited accuracy of model components, and scarcity of observations available for assessing relevant coupled processes. This study presents a newly developed convective-scale atmosphere–wave coupled forecasting system for the European Arctic. The HARMONIE-AROME configuration of the ALADIN-HIRLAM numerical weather prediction system is coupled to the spectral wave model WAVEWATCH III using the OASIS3 model coupling toolkit. We analyze the impact of representing the kilometer-scale atmosphere–wave interactions through coupled and uncoupled forecasts on a model domain with 2.5-km spatial resolution. To assess the coupled model’s accuracy and uncertainties we compare 48-h model forecasts against satellite observational products such as Advanced Scatterometer 10-m wind speed, and altimeter-based significant wave height. The fully coupled atmosphere–wave model results closely match both satellite-based wind speed and significant wave height observations as well as surface pressure and wind speed measurements from selected coastal station observation sites. Furthermore, the coupled model contains smaller standard deviation of errors in both 10-m wind speed and significant wave height parameters when compared to the uncoupled model forecasts. Atmosphere and wave coupling reduces the short-term forecast error variability of 10-m wind speed and significant wave height with the greatest benefit occurring for high wind and wave conditions.

Open access