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Ryan J. Longman, Oliver Elison Timm, Thomas W. Giambelluca, and Lauren Kaiser

Abstract

Undisturbed trade-wind conditions compose the most prevalent synoptic weather pattern in Hawai‘i and produce a distinct pattern of orographic rainfall. Significant total rainfall contributions and extreme events are linked to four types of atmospheric disturbances: cold fronts, kona lows, upper-tropospheric disturbances, and tropical cyclones. In this study, a 20-yr (1990–2010) categorical disturbance time series is compiled and analyzed in relation to daily rainfall over the same period. The primary objective of this research is to determine how disturbances contribute to total wet-season rainfall on the Island of O‘ahu, Hawai‘i. On average, 41% of wet-seasonal rainfall occurs on disturbance days. A total of 17% of seasonal rainfall can be directly attributed to disturbances (after a background signal is removed) and as much as 48% in a single season. The intensity of disturbance rainfall (mm day−1) is a stronger predictor (r 2 = 0.49; p < 0.001) of the total seasonal rainfall than the frequency of occurrence (r 2 = 0.11; p = 0.153). Cold fronts are the most common disturbance type; however, the rainfall associated with fronts that cross the island is significantly higher than rainfall produced from noncrossing fronts. In fact, noncrossing fronts produce significantly less rainfall than under mean nondisturbance conditions 76% of the time. While the combined influence of atmospheric disturbances can account for almost one-half of the rainfall received during the wet season, the primary factor in determining a relatively wet or dry season/year on Oʻahu is the frequency and rainfall intensity of kona low events.

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Alex Schueth, Christopher Weiss, and Johannes M. L. Dahl

Abstract

The forward-flank convergence boundary (FFCB) in supercells has been well documented in many observational and modeling studies. It is theorized that the FFCB is a focal point for the baroclinic generation of vorticity. This vorticity is generally horizontal and streamwise in nature, which can then be tilted and converted to midlevel (3–6 km AGL) vertical vorticity. Previous modeling studies of supercells often show horizontal streamwise vorticity present behind the FFCB, with higher-resolution simulations resolving larger magnitudes of horizontal vorticity. Recently, studies have shown a particularly strong realization of this vorticity called the streamwise vorticity current (SVC). In this study, a tornadic supercell is simulated with the Bryan Cloud Model at 125-m horizontal grid spacing, and a coherent SVC is shown to be present. Simulated range–height indicator (RHI) data show the strongest horizontal vorticity is located on the periphery of a steady-state Kelvin–Helmholtz billow in the FFCB head. Additionally, a similar structure is found in two separate observed cases with the Texas Tech University Ka-band (TTUKa) mobile radar RHIs. Analyzing vorticity budgets for parcels in the vicinity of the FFCB head in the simulation, stretching of vorticity is the primary contributor to the strong streamwise vorticity, while baroclinic generation of vorticity plays a smaller role.

Open access
Stephen Jewson, Sebastian Scher, and Gabriele Messori

Abstract

Users of meteorological forecasts are often faced with the question of whether to make a decision now, on the basis of the current forecast, or to wait for the next and, it is hoped, more accurate forecast before making the decision. Following previous authors, we analyze this question as an extension of the well-known cost–loss model. Within this extended cost–loss model, the question of whether to decide now or to wait depends on two specific aspects of the forecast, both of which involve probabilities of probabilities. For the special case of weather and climate forecasts in the form of normal distributions, we derive a simple simulation algorithm, and equivalent analytical expressions, for calculating these two probabilities. We apply the algorithm to forecasts of temperature and find that the algorithm leads to better decisions in most cases relative to three simpler alternative decision-making schemes, in both a simulated context and when we use reforecasts, surface observations, and rigorous out-of-sample validation of the decisions. To the best of our knowledge, this is the first time that a dynamic multistage decision algorithm has been demonstrated to work using real weather observations. Our results have implications for the additional kinds of information that forecasters of weather and climate could produce to facilitate good decision-making on the basis of their forecasts.

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Peng Wang, Yishuai Jin, and Zhengyu Liu

Abstract

In this study, we investigate a diurnal predictability barrier (DPB) for weather predictions using an idealized model and observations. This DPB is referred to a maximum drop of predictability (e.g., autocorrelation) at a particular time of the day, regardless of the initial time. Previous studies demonstrated that a strong seasonal cycle of El Niño–Southern Oscillation (ENSO) growth rate is responsible for the seasonal predictability barrier of the ENSO in spring. This led us to investigate whether or not a strong diurnal cycle may generate a DPB. We study the DPB using an idealized model, the Lorenz 1963 model, with the addition of a diurnal cycle. We find that diurnal growth rate can generate a DPB in this chaotic system, regardless of the initial error. Finally, by calculating the autocorrelation function using the hourly data of surface temperature, we explore the DPB at two stations in Wisconsin and Beijing, China. A clear DPB feature is found at both stations. The dramatic drop of predictability at a specific time of the day is likely due to the diurnal variation of the system. This is a new feature that needs further study for short-term weather predictions.

Open access
Samar Minallah and Allison L. Steiner

Abstract

Lakes are an integral part of the geosphere, but they are challenging to represent in Earth system models, which either exclude lakes or prescribe properties without simulating lake dynamics. In the ECMWF interim reanalysis (ERA-Interim), lakes are represented by prescribing lake surface water temperatures (LSWT) from external data sources, while the newer-generation ERA5 introduces the Freshwater Lake (FLake) parameterization scheme to the modeling system with different LSWT assimilation data sources. This study assesses the performance of these two reanalyses over three regions with the largest lakes in the world (Laurentian Great Lakes, African Great Lakes, and Lake Baikal) to understand the effects of their simulation differences on hydrometeorological variables. We find that differences in lake representation alter the associated hydrological and atmospheric processes and can affect regional hydroclimatic assessments. There are prominent differences in LSWT between the two datasets that influence the simulation of lake-effect snowstorms in the Laurentian winters and lake–land-breeze circulation patterns in the African region. Generally, ERA5 has warmer LSWT in all three regions for most months (by 2–12 K) and its evaporation rates are up to twice the magnitudes in ERA-Interim. In the Laurentian lakes, ERA5 has strong biases in LSWT and evaporation magnitudes. Over Lake Baikal and the African Great Lakes, ERA5 LSWT magnitudes are closer to satellite-based datasets, albeit with a warm bias (1–4 K), while ERA-Interim underestimates the magnitudes. ERA5 also simulates intense precipitation hot spots in lake proximity that are not visible in ERA-Interim and other observation-based datasets. Despite these limitations, ERA5 improves the simulation of lake–land circulation patterns across the African Great Lakes.

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Yang Liu, Laurens Bogaardt, Jisk Attema, and Wilco Hazeleger

Abstract

Operational Arctic sea ice forecasts are of crucial importance to science and to society in the Arctic region. Currently, statistical and numerical climate models are widely used to generate the Arctic sea ice forecasts at weather time scales. Numerical models require near-real-time input of relevant environmental conditions consistent with the model equations and they are computationally expensive. In this study, we propose a deep learning approach, namely convolutional long short-term memory networks (ConvLSTM), to forecast sea ice in the Barents Sea at weather to subseasonal time scales. This is an unsupervised learning approach. It makes use of historical records and it exploits the covariances between different variables, including spatial and temporal relations. With input fields from reanalysis data, we demonstrate that ConvLSTM is able to learn the variability of the Arctic sea ice and can forecast regional sea ice concentration skillfully at weekly to monthly time scales. It preserves the physical consistency between predictors and predictands, and generally outperforms forecasts with climatology, persistence, and a statistical model. Based on the known sources of predictability, sensitivity tests with different climate fields as input for learning were performed. The impact of different predictors on the quality of the forecasts are evaluated and we demonstrate that the surface energy budget components have a large impact on the predictability of sea ice at weather time scales. This method is a promising way to enhance operational Arctic sea ice forecasting in the near future.

Open access
Erin B. Munsell, Scott A. Braun, and Fuqing Zhang

Abstract

This study utilizes brightness temperatures (T bs) observed by the infrared longwave window band (channel 14; 11.2 μm) from the Geostationary Operational Environmental Satellite-16 (GOES-16) to examine the structure of Hurricanes Harvey, Maria, and Michael throughout their lifetimes. During the times leading up to their rapid intensifications (RI), two-dimensional inner-core structures are examined to analyze the strength and location of the developing convection. Moderate vertical wind shear in the environments of Harvey and Michael induced a pronounced convective asymmetry prior to RI, followed by a rapid axisymmetrization that occurred essentially in conjunction with RI. The evolutions of the tropical cyclones’ (TCs’) coldest T bs indicate that the inner-core convective activity began to increase in the 12 h prior to RI onset, primarily in 2–4-h substantial “bursts,” while substantial convection dominated essentially the entirety of the region within 100 km of the surface center within 12 h of the onset of intensification. Azimuthally averaged T b evolutions illustrate the development of each TC’s eye and eyewall, the variability of the radial extent of the central dense overcast associated with the diurnal cycle, as well as details of the evolving convective structures throughout intensification. Hovmöller diagrams of data at constant radii reveal areas of cold T bs propagating around the TCs on time scales of 2–3 h. The examination of these features in a deep-layer shear-relative sense reveals that they initiate primarily downshear of the TCs’ surface centers. As RI is reached, these areas of convection are able to propagate into the upshear quadrants, which helps facilitate the onset of more substantial intensification.

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John R. Mecikalski, Thea N. Sandmæl, Elisa M. Murillo, Cameron R. Homeyer, Kristopher M. Bedka, Jason M. Apke, and Chris P. Jewett

Abstract

Few studies have assessed combined satellite, lightning, and radar databases to diagnose severe storm potential. The research goal here is to evaluate next-generation, 60-s update frequency geostationary satellite and lightning information with ground-based radar to isolate which variables, when used in concert, provide skillful discriminatory information for identifying severe (hail ≥ 2.5 cm in diameter, winds ≥ 25 m s−1, and tornadoes) versus nonsevere storms. The focus of this study is predicting severe thunderstorm and tornado warnings. A total of 2004 storms in 2014–15 were objectively tracked with 49 potential predictor fields related to May, daytime Great Plains convective storms. All storms occurred when 1-min Geostationary Operational Environmental Satellite (GOES)-14 “super rapid scan” data were available. The study used three importance methods to assess predictor importance related to severe warnings and used random forests to provide a model and skill evaluation measuring the ability to predict severe storms. Three predictor importance methods show that GOES mesoscale atmospheric-motion-vector-derived cloud-top divergence and above-anvil cirrus plume presence provide the most satellite-based discriminatory power for diagnosing severe warnings. Other important fields include Earth Networks Total Lightning flash density, GOES estimated cloud-top vorticity, and overshooting-top presence. Severe warning predictions are significantly improved at the 95% confidence level when a few important satellite and lightning fields are combined with radar fields, versus when only radar data are used in the random-forest model. This study provides a basis for including satellite and lightning fields within machine-learning models to help forecast severe weather.

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Kenta Kurosawa and Jonathan Poterjoy

Abstract

The ensemble Kalman Filter (EnKF) and the 4D variational method (4DVar) are the most commonly used filters and smoothers in atmospheric science. These methods typically approximate prior densities using a Gaussian and solve a linear system of equations for the posterior mean and covariance. Therefore, strongly nonlinear model dynamics and measurement operators can lead to bias in posterior estimates. To improve the performance in nonlinear regimes, minimization of the 4DVar cost function typically follows multiple sets of iterations, known as an “outer loop”, which helps reduce bias caused by linear assumptions. Alternatively, “iterative ensemble methods” follow a similar strategy of periodically re-linearizing model and measurement operators. These methods come with different, possibly more appropriate, assumptions for drawing samples from the posterior density, but have seen little attention in numerical weather prediction (NWP) communities. Lastly, particle filters (PFs) present a purely Bayesian filtering approach for state estimation, which avoids many of the assumptions made by the above methods. Several strategies for applying localized PFs for NWP have been proposed very recently. The current study investigates intrinsic limitations of current data assimilation methodology for applications that require nonlinear measurement operators. In doing so, it targets a specific problem that is relevant to the assimilation of remotely-sensed measurements, such as radar reflectivity and all-sky radiances, which pose challenges for Gaussian-based data assimilation systems. This comparison includes multiple data assimilation approaches designed recently for nonlinear/non-Gaussian applications, as well as those currently used for NWP.

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Benjamin J. Moore, Allen B. White, and Daniel J. Gottas

Abstract

Prolonged periods (e.g., several days or more) of heavy precipitation can result in sustained high-impact flooding. Herein, an investigation of long-duration heavy precipitation events (HPEs), defined as periods comprising ≥ 3 days with precipitation exceeding the climatological 95th percentile, is conducted for 1979–2019 for the U.S. West Coast, specifically Northern California. An objective flow-based categorization method is applied to identify principal large-scale flow patterns for the events. Four categories are identified and examined through composite analyses and case studies. Two of the categories are characterized by a strong zonal jet stream over the eastern North Pacific, while the other two are characterized by atmospheric blocking over the central North Pacific and the Bering Sea–Alaska region, respectively. The composites and case studies demonstrate that the flow patterns for the HPEs tend to remain in place for several days, maintaining strong baroclinicity and promoting occurrences of multiple cyclones in rapid succession near the West Coast. The successive cyclones result in persistent water vapor flux and forcing for ascent over Northern California, sustaining heavy precipitation. For the zonal jet patterns, cyclones affecting the West Coast tend to occur in the poleward jet exit region in association with cyclonic Rossby wave breaking. For the blocking patterns, cyclones tend to occur in association with anticyclonic Rossby wave breaking on the downstream flank of the block. For Bering Sea–Alaska blocking cases, cyclones can move into this region in conjunction with cyclonically breaking waves that extend into the eastern North Pacific from the upstream flank of the block.

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