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Ryan Heintzman, Robert C. Balling Jr., and Randall S. Cerveny

Abstract

A new amalgamation of weather stations in and around Joshua Tree National Park in southeastern California has allowed for objective climate analysis regionalization at a much finer scale than past studies. First, it sets a baseline for many regions within the park’s boundaries that were not subject to direct observations. Second, these new observations are key to understanding shifting microclimate regimes in a desert ecosystem prone to the effects of climate change. Principal component analysis was used to regionalize the climate network based on monthly temperature and precipitation climate observations and standardized anomalies. Both the observation values and standardized climate anomalies identified regional boundaries. In general, these boundaries align with traditional ideas and past studies of the Mojave and Sonoran Deserts based on elevation (specifically the 1000-m contour) for the National Park Service. Standardized anomaly values identified a boundary based on seasonal precipitation, whereas observation values identified a boundary based on elevation. The boundary line within the park is similar for both data approaches, with the boundary running along the higher western one-third of the park. Conversely, the two methods differ significantly in the Coachella Valley, where low elevations and low precipitation meet winter-dominated seasonal precipitation. This study highlights the importance and opportunity of field observations to create climatological and ecological regionalization, and it also constructs a baseline to monitor and manage shifting desert regions in the future.

Significance Statement

This study identifies a high-resolution climate boundary zone in Joshua Tree National Park between the Sonoran and Mojave Deserts. The new transition zone presents the seasonal and elevational temperature and precipitation components of the two deserts, connecting with the unique ecology of the deserts. This finding highlights just one study opportunity of new field observation networks in arid or topographically diverse regions. It also provides a baseline for climate change as a resource for environmental management groups to better understand and preserve our natural spaces.

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Tao Song, Ying Li, Fan Meng, Pengfei Xie, and Danya Xu

Abstract

Tropical cyclones are among the most powerful and destructive meteorological systems on Earth. In this paper, we propose a novel deep learning model for tropical cyclone track prediction method. Specifically, the track task is regarded as a time series predicting challenge, and then a deep learning framework by a bidirectional gate recurrent unit network (BiGRU) with attention mechanism is developed for track prediction. This proposed model can excavate the effective information of the historical track in a deeper and more accurate way. Data experiments are conducted on tropical cyclone best-track data provided by the Joint Typhoon Warning Center (JTWC) from 1988 to 2017 in the northwestern Pacific Ocean. Results show that our model performs well for tracks of 6, 12, 24, 48, and 72 h in the future. The prediction results show that our proposed combined model is superior to state-of-the-art deep learning models, including a recurrent neural network (RNN), long short-term memory neural network (LSTM), gate recurrent unit network (GRU), and BiGRU without the use of attention mechanism. In comparison with the methods used by the China Meteorological Administration, Japan Meteorological Agency, and the JTWC, our method has obvious advantages in the mid- to long-term track forecasting, especially in the next 72 h.

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Connor J. Chapman and Andrew M. Carleton

Abstract

Recent climatic studies for the dominantly rain-fed agricultural U.S. Corn Belt (CB) suggest an influence of land-use/land-cover (LULC) spatial differences on convective development, set within the larger-scale (synoptic) atmospheric conditions of pressure, winds, and vertical motion. However, the potential role of soil moisture (SM) in the LULC association with atmospheric humidity, horizontal wind, and convective precipitation (CVP) has received more limited attention, mostly as modeling studies or empirical analyses for regions nonanalogous to the CB. Accordingly, we determine the categorical associations between SM and the near-surface atmospheric humidity q, with 850-hPa horizontal wind V 850 at four representative CB locations for the nine warm seasons of 2011–19. Recurring configurations of joint SM–qV 850 conducive to CVP are then identified and stratified into three phenologically distinct subseasons (early, middle, and late). We show that the stations show some statistical similarity in their SM–CVP relationships. Corn Belt CVP occurs preferentially with high humidity and southerly winds, sometimes composing a low-level jet (LLJ), particularly on early-season days having low SM and late-season days having high SM. Additionally, midseason CVP days having weaker V 850 (i.e., non-LLJ) tend to be associated with medium SM values and high humidity. Conversely, late-season CVP days are frequently characterized by high values of both SM and humidity. These empirical results are likely explained by the inferred sensible and latent heat fluxes varying according to SM content and LULC type. They provide a basis for future mesoscale modeling studies of Corn Belt SM and CVP interactions to test the hypothesized physical processes.

Significance Statement

The effects of soil moisture on precipitation are not well understood, as previous research has found contrasting results depending on study region and period of focus. We determine these associations for the Corn Belt, a humid lowland region that has received less attention than the drier neighboring Great Plains. Our study finds strong soil moisture–precipitation relationships in the presence of high humidity, which may be explained by mechanisms associated with the subseasonal cycle of vegetation activity. Additionally, our results suggest a generally weaker influence of soil moisture on precipitation for the Corn Belt than for the Great Plains, highlighting the importance of understanding how these relationships vary spatially. Future work should test the inferred surface–atmosphere mechanisms introduced here using mesoscale modeling.

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Q. Huang, W. J. Jiang, and H. P. Hong

Abstract

Canada experiences a relatively large number of tornadoes, which can cause a significant amount of damage and fatalities. In this study, a preferred prediction model for the spatially varying tornado occurrence rate is developed for Canada. The development takes into account the most commonly used spatial stochastic models and the underreporting that is due to low population density. It incorporates the annual average cloud-to-ground lightning flash (ACGLF) density and annual average thunderstorm days (ATD) as covariates in the prediction model. The model parameters estimation is carried out by using both the maximum likelihood method and the Bayesian inference. The analysis results indicate that the negative binomial model is preferable to the zero-inflated Poisson model and the Poisson model. The results show that tornado occurrence in Canada is associated with large overdispersion. Also, the statistical analysis indicates that the prediction model for the tornado occurrence rate developed on the basis of Bayesian inference is relatively insensitive to the assumed “noninformative” prior distributions. A prediction model is suggested for the spatially varying tornado occurrence rate based on the negative binomial model with the ACGLF density and ATD as covariates.

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Stephen Jewson

Abstract

Knutson et al. recently published a metastudy that gives multimodel projections for changes in various properties of tropical cyclones under climate change. They considered frequency of tropical cyclones, frequency of very intense tropical cyclones, intensity of tropical cyclones, and total rainfall rate of tropical cyclones. For each of these properties, they reported changes globally and by basin for the six major tropical cyclone basins. The changes were presented as the change that would occur with 2°C warming of global mean surface temperature. These projections are potentially of great use to the tropical cyclone risk modeling community. However, most risk models use temporal baselines, such as the period from 1950 to 2019, and the Knutson et al. results can only be applied to risk models after some steps of adjustment involving past and future global mean surface temperature values. We derive the necessary adjustments and present and discuss some of the resulting projections, for different properties, basins, RCPs, and baselines. We find that the results are sensitive to the baseline being used, which implies that users of tropical cyclone risk models need to make sure they clearly understand what baseline their model represents before they adjust the model for climate change. One part of our analysis derives estimates of the implied impact of climate change so far on TC properties, relative to a representative baseline. The computer code we use to calculate the adjustments is available online.

Open access
Catherine M. Naud, Juan A. Crespo, and Derek J. Posselt

Abstract

Surface latent and sensible heat fluxes are important for extratropical cyclone evolution and intensification. Because extratropical cyclone genesis often occurs at low latitudes, Cyclone Global Navigation Satellite System (CYGNSS) surface latent and sensible heat flux retrievals are composited to provide a mean picture of their spatial distribution in low-latitude oceanic extratropical cyclones. CYGNSS heat fluxes are not affected by heavy precipitation and offer observations of storms with frequent revisit times. Consistent with prior results obtained for cyclones in the Gulf Stream region, the fluxes are strongest in the wake of the cold fronts and are weakest to negative in the warm sector in advance of the cold fronts. As cyclone strength increases or mean precipitable water decreases, the maximum in surface heat fluxes increases while the minimum decreases. This affects the changes in fluxes during cyclone intensification: the post-cold-frontal surface heat flux maximum increases as a result of the increase in near-surface winds. During cyclone dissipation, the fluxes in this sector decrease because of the decrease in winds and in temperature and humidity contrast. The warm-sector minimum decreases throughout the entire cyclone lifetime and is mostly driven by sea–air temperature and humidity contrast changes. However, during cyclone dissipation, the surface heat fluxes increase along the cold front in a narrow band to the east, independent from changes in the cyclone characteristics. This result suggests that, during cyclone dissipation, energy transfers from the ocean to the atmosphere are linked to frontal processes in addition to synoptic-scale processes.

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Bhupal Shrestha, J. A. Brotzge, J. Wang, N. Bain, C. D. Thorncroft, E. Joseph, J. Freedman, and S. Perez

Abstract

Vertical profiles of atmospheric temperature, moisture, wind, and aerosols are essential information for weather monitoring and prediction. Their availability, however, is limited in space and time because of the significant resources required to observe them. To fill this gap, the New York State Mesonet (NYSM) Profiler Network has been deployed as a national testbed to facilitate the research, development, and evaluation of ground-based profiling technologies and applications. The testbed comprises 17 profiler stations across the state, forming a long-term regional observational network. Each profiler station comprises a ground-based Doppler lidar, a microwave radiometer (MWR), and an environmental Sky Imager–Radiometer (eSIR). Thermodynamic profiles (temperature and humidity) from the MWR, wind and aerosol profiles from the Doppler lidar, and solar radiance and optical depth parameters from the eSIR are collected, processed, disseminated, and archived every 10 min. This paper introduces the NYSM Profiler Network and reviews the network design and siting, instrumentation, network operations and maintenance, data and products, and some example applications that highlight the benefits of the network. Some sample applications include improved situational awareness and monitoring of the sea–land breeze, long-range wildfire smoke transport, air quality (PM2.5 and aerosol optical depth) and boundary layer height. Ground-based profiling systems promise a path forward for filling a critical gap in the U.S. observing system with the potential to improve analysis and prediction for many weather-sensitive sectors, such as aviation, ground transportation, health, and wind energy.

Significance Statement

The New York State Mesonet (NYSM) Profiler Network enables routine measurement of aboveground weather data and products to monitor weather and air quality across the state at high resolutions. The NYSM Profiler Network provides real-time vertical profile information to users across the emergency management, aviation, utility, and public health sectors, including NOAA and NASA, for operations and research, filling a critical gap in monitoring the low-level atmosphere. These data have been used to improve situational awareness and monitor boundary layer dynamics, sea-land breeze development, precipitation type, and air quality. Most important, the NYSM Profiler Network provides a national testbed for the creation and evaluation of new ground-based profiling instrumentation and products.

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Chandra Rupa Rajulapati, Simon Michael Papalexiou, Martyn P. Clark, and John W. Pomeroy

Abstract

Gridded precipitation datasets are used in many applications such as the analysis of climate variability/change and hydrological modeling. Regridding precipitation datasets is common for model coupling (e.g., coupling atmospheric and hydrological models) or comparing different models and datasets. However, regridding can considerably alter precipitation statistics. In this global analysis, the effects of regridding a precipitation dataset are emphasized using three regridding methods (first-order conservative, bilinear, and distance-weighted averaging). The differences between the original and regridded dataset are substantial and greatest at high quantiles. Differences of 46 and 0.13 mm are noted in high (0.95) and low (0.05) quantiles, respectively. The impacts of regridding vary spatially for land and oceanic regions; there are substantial differences at high quantiles in tropical land regions, and at low quantiles in polar regions. These impacts are approximately the same for different regridding methods. The differences increase with the size of the grid at higher quantiles and vice versa for low quantiles. As the grid resolution increases, the difference between original and regridded data declines, yet the shift size dominates for high quantiles for which the differences are higher. While regridding is often necessary to use gridded precipitation datasets, it should be used with great caution for fine resolutions (e.g., daily and subdaily), because it can severely alter the statistical properties of precipitation, specifically at high and low quantiles.

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Ioannis Cheliotis, Elsa Dieudonné, Hervé Delbarre, Anton Sokolov, Egor Dmitriev, Patrick Augustin, Marc Fourmentin, François Ravetta, and Jacques Pelon

Abstract

The studies related to the coherent structures in the atmosphere, using Doppler wind lidar observations, so far have relied on the manual detection and classification of the structures in the lidar images, making this process time-consuming. We developed an automated classification that is based on texture analysis parameters and the quadratic discriminant analysis algorithm for the detection of medium-to-large fluctuations and coherent structures recorded by single Doppler wind lidar quasi-horizontal scans. The algorithm classified a training dataset of 150 cases into four types of patterns, namely, streaks (narrow stripes), rolls (wide stripes), thermals (enclosed areas), and “others” (impossible to classify), with 91% accuracy. Subsequently, we applied the trained algorithm to a dataset of 4577 lidar scans recorded in Paris, atop a 75-m tower for a 2-month period (September–October 2014). The current study assesses the quality of the classification by examining the physical properties of the classified cases. The results show a realistic classification of the data: with rolls and thermals cases mostly classified concurrently with a well-developed atmospheric boundary layer and the streaks cases associated with nocturnal low-level jets events. Furthermore, rolls and streaks cases were mostly observed under moderate or high wind conditions. The detailed analysis of a 4-day period reveals the transition between the types. The analysis of the space spectra in the direction transverse to the mean wind, during these four days, revealed streak spacing of 200–400 m and roll sizes, as observed in the lower level of the mixed layer, of approximately 1 km.

Open access
James F. Booth, Veeshan Narinesingh, Katherine L. Towey, and Jeyavinoth Jeyaratnam

Abstract

Storm surge is a weather hazard that can generate dangerous flooding and is not fully understood in terms of timing and atmospheric forcing. Using observations along the northeastern United States, surge is sorted on the basis of duration and intensity to reveal distinct time-evolving behavior. Long-duration surge events slowly recede, whereas strong, short-duration events often involve negative surge in quick succession after the maximum. Using Lagrangian track information, the tropical and extratropical cyclones and atmospheric blocks that generate the surge events are identified. There is a linear correlation between surge duration and surge maximum, and the relationship is stronger for surge caused by extratropical cyclones as compared with those events caused by tropical cyclones. For the extremes based on duration, the shortest-duration strong surge events are caused by tropical cyclones, whereas the longest-duration events are most often caused by extratropical cyclones. At least one-half of long-duration surge events involve anomalously strong atmospheric blocking poleward of the cyclone, whereas strong, short-duration events are most often caused by cyclones in the absence of blocking. The dynamical influence of the blocks leads to slow-moving cyclones that take meandering paths. In contrast, for strong, short-duration events, cyclones travel faster and take a more meridional path. These unique dynamical scenarios provide better insight for interpreting the threat of surge in medium-range forecasts.

Open access