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Rouyi Jiang
,
Xiaopeng Cui
,
Jian Lin
, and
Jia Tian

Abstract

Southwest China (SWC) possesses complicated topography with frequent geological activities, where heavy precipitation occurs frequently in warm seasons. Few previous studies on extreme precipitation were carried out at hourly scales. In this study, spatiotemporal variations of the extreme hourly precipitation (EHP) over SWC during the warm season of 1981–2020 and the involved mechanisms are investigated. Results show that the threshold and intensity of EHP present similar spatial distribution—lower (higher) in the west (east) part of SWC, while the EHP frequency is opposite. The long-term trend of EHP amount shows a more significant positive tendency than that of hourly precipitation (HP) amount due to synchronous increases in intensity and frequency. The significant increasing trend of EHP occurs in areas above 500-m terrain height, with a weak increasing trend below 500 m (e.g., Chongqing and eastern Sichuan). EHP appears mainly from June to August and exhibits a bimodal distribution in diurnal variation. The mechanism analysis demonstrates that occurrences of EHP are generally accompanied by positive anomalies of temperature, humidity, and geopotential height. Anomalous cyclonic circulation can also be found in the low-level wind field. The westward and northward extension of the western North Pacific subtropical high (WNPSH) as well as temperature rise may be the primary reason for the increase of EHP. For Chongqing and eastern Sichuan, the anticyclone circulation in low-level and the significantly weakened water vapor flux convergence cause poor moisture and dynamic conditions, inhibiting the growth of EHP.

Significance Statement

Heavy precipitation occurs frequently during the warm season in Southwest China (SWC), often causing severe impacts on human safety and economic property. This study analyses spatiotemporal variations of the extreme hourly precipitation (EHP) over SWC during the warm season of 1981–2020 and the involved mechanisms. The increasing trend of EHP far exceeds that of hourly precipitation (HP), especially in areas above 500 m. The westward and northward extension of the western North Pacific subtropical high (WNPSH) and temperature rise may be the main reason for the increase of EHP. For areas below 500 m (e.g., Chongqing and eastern Sichuan), poor moisture and dynamic conditions inhibited the growth of EHP.

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Megan S. Mallard
,
Kevin D. Talgo
,
Tanya L. Spero
,
Jared H. Bowden
, and
Christopher G. Nolte

Abstract

Phenological indicators (PI) are used to study changes to animal and plant behavior in response to seasonal cycles, and they can be useful to quantify the potential impacts of climate change on ecosystems. Here, multiple global climate models and emission scenarios are used to drive dynamically downscaled simulations using the WRF Model over the contiguous United States (CONUS). The wintertime dormancy of plants [chilling units (CU)], timing of spring onset [extended spring indices (SI)], and frequency of proceeding false springs are calculated from regional climate simulations covering historical (1995–2005) and future periods (2025–2100). Southern parts of the CONUS show projected CU decreases (inhibiting some plants from flowering or fruiting), while the northern CONUS experiences an increase (possibly causing plants to break dormancy too early, becoming vulnerable to disease or freezing). Spring advancement (earlier SI dates) is projected, with decadal trends ranging from approximately 1–4 days per decade over the CONUS, comparable to or exceeding those found in observational studies. Projected changes in risk of false spring (hard freezes following spring onset) vary across members of the ensemble and regions of the CONUS, but generally western parts of the CONUS are projected to experience increased risk of false springs. These projected changes to PI connote significant effects on cycles of plants, animals, and ecosystems, highlighting the importance of examining temperature changes during transitional seasons.

Significance Statement

This study examines how phenological indicators, which track the life cycles of plants and animals, could change from 2025 to 2100 as simulated in a regional climate model over the contiguous United States. Chilling units quantify the presence of cooler weather that can benefit plants prior to their growing season. They are projected to decrease in the southern United States, possibly inhibiting agricultural production. Spring onset is projected to occur earlier in the year, advancing by 1–4 days on average over each future decade. Risk of false springs (damaging hard freezes after spring onset) increases in the western United States. Our findings highlight the need to understand effects of climate change during transitional seasons, which can impact agriculture and ecosystems.

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Darryn W. Waugh
,
Benjamin Zaitchik
,
Anna A. Scott
,
Peter C. Ibsen
,
G. Darrel Jenerette
,
Jason Schatz
, and
Christopher J. Kucharik

Abstract

Monitoring and understanding the variability of heat within cities is important for urban planning and public health, and the number of studies measuring intraurban temperature variability is growing. Recognizing that the physiological effects of heat depend on humidity as well as temperature, measurement campaigns have included measurements of relative humidity alongside temperature. However, the role the spatial structure in humidity, independent from temperature, plays in intraurban heat variability is unknown. Here we use summer temperature and humidity from networks of stationary sensors in multiple cities in the United States to show spatial variations in the absolute humidity within these cities are weak. This variability in absolute humidity plays an insignificant role in the spatial variability of the heat index and humidity index (humidex), and the spatial variability of the heat metrics is dominated by temperature variability. Thus, results from previous studies that considered only intraurban variability in temperature will carry over to intraurban heat variability. Also, this suggests increases in humidity from green infrastructure interventions designed to reduce temperature will be minimal. In addition, a network of sensors that only measures temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location, allowing for lower-cost heat monitoring networks.

Significance Statement

Monitoring the variability of heat within cities is important for urban planning and public health. While the physiological effects of heat depend on temperature and humidity, it is shown that there are only weak spatial variations in the absolute humidity within nine U.S. cities, and the spatial variability of heat metrics is dominated by temperature variability. This suggests increases in humidity will be minimal resulting from green infrastructure interventions designed to reduce temperature. It also means a network of sensors that only measure temperature is sufficient to quantify the spatial variability of heat across these cities when combined with humidity measured at a single location.

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S. Jamaer
,
D. Allaerts
,
J. Meyers
, and
N. P. M. van Lipzig

Abstract

Vertical temperature profiles influence the wind power generation of large offshore wind farms through stability-dependent effects such as blockage and gravity waves. However, numerical tools that are used to model these effects are often computationally too expensive to cover the large variety of atmospheric states occurring over time. Generally, an informed decision about which representative nonidealized situations to simulate is missing because of the lack of easily available information on representative vertical profiles, taking into account their spatiotemporal variability. Therefore, we present a novel framework that allows a smart selection of vertical temperature profiles. The framework consists of an improved analytical temperature model for the atmospheric boundary layer and lower troposphere, a subsequent clustering of these profiles to identify representatives, and last, a determination of areas with similar spatiotemporal characteristics of vertical profiles. When applying this framework on European ERA5 data, physically realistic representatives were identified for Europe, excluding the Mediterranean. Two or three profiles were found to be dominant for the open ocean, whereas more profiles prevail for land. Over the open ocean, weak temperature gradients in the boundary layer and a clear capping inversions are widespread, and stable profiles are absent except in the region of the East Icelandic Current. Interestingly, according to the ERA5 data, at its resolution, coastal areas and seas surrounded by land behave more similar to the land areas than to the open ocean, implying that a larger set of model integrations are needed for these areas to obtain representative results for offshore wind power assessments in comparison with the open ocean.

Significance Statement

Numerical tools used to simulate the effect of large, offshore wind farms on neighboring farms and the atmosphere are very expensive. Therefore, they can only be computed for a limited number of cases. As temperature is one of the most important parameters in these kinds of simulations, this work provides a new vertical temperature model and an analysis framework that allows for a smart selection of these cases such that they ideally represent the full variation of the atmosphere’s temperature profiles.

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Andrew J. Heymsfield
,
Micael A. Cecchini
,
Andrew Detwiler
,
Ryan Honeyager
, and
Paul Field

Abstract

Measurements from the South Dakota School of Mines and Technology T-28 hail-penetrating aircraft are analyzed using recently developed data processing techniques with the goals of identifying where the large hail is found relative to vertical motion and improving the detection of hail microphysical properties from radar. Hail particle size distributions (PSD) and environmental conditions (temperature, relative humidity, liquid water content, air vertical velocity) were digitally collected by the T28 between 1995 and 2003 and synthesized by Detwiler et al. The PSD were forward modeled by Cecchini et al. to simulate the radar reflectivity of the PSD at multiple radar wavelengths. The T-28 penetrated temperatures primarily between 0° and −10°C. The largest hailstones were sampled near the updraft/downdraft interface. Liquid water contents were highest in the updraft cores, whereas total (liquid + frozen) water contents were highest near the updraft/downdraft interface. The fitted properties of the PSD (intercept and slope) are directly related to each other but do not show any dependence on the region of the hailstorm where sampled. The PSD measurements and the radar reflectivity calculations at multiple radar wavelengths facilitated the development of relationships between the PSD bulk properties—hail kinetic energy and kinetic energy flux—and the radar reflectivity. Rather than using the oft-assumed sphericity and solid ice physical properties, actual measurements of hail properties are used in the analysis. Results from the maximum estimated size of hail (MESH) and vertical integrated liquid water (VIL) algorithms are evaluated based on this analysis.

Significance Statement

Hailstorms in the United States have caused over $10 billion in damage for each of the last 14 years, according to insurance industry estimates (Heymsfield and Giammanco 2020). Algorithms have been developed to identify the presence and size of hail from radar. Numerical simulations of hailstorms have improved significantly since the 1970s, and further improvements will provide better resolution and more accurate estimates of the sizes of hailstones falling to the ground. Measurements of the properties of hailstones—their mass and terminal velocities—have been improved in recent years but in general are not incorporated in the algorithms developed for radar estimates of hail sizes or for the properties of hail used in the model simulations. This study synthesizes in situ aircraft data, computed radar backscatter cross sections, together with recent estimates of the physical characteristics of hailstones to improve the representation of hail in numerical models and quantitative assessment hail properties in storms using weather radar.

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Stephen Jewson

Abstract

We use a simple risk model for U.S. hurricane wind and surge economic damage to investigate the impact of projected changes in the frequencies of hurricanes of different intensities due to climate change. For average annual damage, we find that changes in the frequency of category-4 storms dominate. For distributions of annual damage, we find that changes in the frequency of category-4 storms again dominate for all except the shortest return periods. Sensitivity tests show that accounting for landfall, uncertainties, and correlations leads to increases in damage estimates. When we propagate the distributions of uncertain frequency changes to give a best estimate of the changes in damage, the changes are moderate. When we pick individual scenarios from within the distributions of frequency changes, we find a significant probability of much larger changes in damage. The inputs on which our study depends are highly uncertain, and our methods are approximate, leading to high levels of uncertainty in our results. Also, the damage changes we consider are only part of the total possible change in hurricane damage due to climate change. Total damage change estimates would also need to include changes due to other factors, including possible changes in genesis, tracks, size, forward speed, sea level, rainfall, and exposure. Nevertheless, we believe that our results give important new insights into U.S. hurricane risk under climate change.

Significance Statement

We investigate how changes in the frequencies of hurricanes of different intensities as a result of climate change may contribute to changes in U.S. economic damage due to wind and surge. We find that economic damage will likely increase as a result of projected increases in the frequency of landfalling hurricanes. Analysis of our results shows that increases in the frequency of category-4 storms are the main driver of the changes. Our best estimate results, based on a multimodel ensemble, give modest increases in damage, but within the ensemble there are individual scenarios that give much larger increases in damage. The large range of individual damage estimates is a motivation for continuing efforts to reduce the uncertainty around hurricane projections under climate change.

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Takuto Sato
and
Hiroyuki Kusaka

Abstract

This study focuses on the application of two standard inflow turbulence generation methods for growing convective boundary layer (CBL) simulations: the recycle–rescale (R-R) and the digital filter–based (DF) methods, which are used in computational fluid dynamics. The primary objective of this study is to expand the applicability of the R-R method to simulations of thermally driven CBLs. This method is called the extended R-R method. However, in previous studies, the DF method has been extended to generate potential temperature perturbations. This study investigated whether the extended DF method can be applied to simulations of growing thermally driven CBLs. In this study, idealized simulations of growing thermally driven CBLs using the extended R-R and DF methods were performed. The results showed that both extended methods could capture the characteristics of thermally driven CBLs. The extended R-R method reproduced turbulence in thermally driven CBLs better than the extended DF method in the spectrum and histogram of vertical wind speed. However, the height of the thermally driven CBL was underestimated in about 100 m compared with the extended DF method. Sensitivity experiments were conducted on the parameters used in the extended DF and R-R methods. The results showed that underestimation of the length scale in the extended DF method causes a shortage of large-scale turbulence components. The other point suggested by the results of the sensitivity experiments is that the length of the driver region in the extended R-R method should be sufficient to reproduce the spanwise movement of the roll vortices.

Significance Statement

Inflow turbulence generation methods for large-eddy simulation (LES) models are crucial for the better downscaling of meteorological mesoscale models (RANS models) to microscale models (LES models). Various CFD methods have been developed, but few have been applied to simulations of thermally driven convective boundary layers (CBLs). To address this problem, we focused on a method that recycles turbulence [the recycle–rescale (R-R) method] and another method that synthetically generates turbulence [the digital filter–based (DF) method]. This study extends the R-R method to manage turbulence in thermally driven CBLs. In addition, this study investigated the applicability of the DF method to thermally driven CBL simulations. Both extended methods are effective for downscaling experiments and capture the characteristics of thermally driven CBLs.

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Linye Song
,
Lu Yang
,
Conglan Cheng
,
Aru Hasi
, and
Mingxuan Chen

Abstract

This study investigates the impacts of grid spacing and station network on surface analyses and forecasts including temperature, humidity, and winds in Beijing Winter Olympic complex terrain. The high-resolution analyses are generated by a rapid-refresh integrated system that includes a topographic downscaling procedure. Results show that surface analyses are more accurate with a higher targeted grid spacing. In particular, the average analysis errors of surface temperature, humidity, and winds are all significantly reduced when the grid size is increased. This improvement is mainly attributed to a more realistic simulation of the topographic effects in the integrated system because the topographic downscaling at higher grid spacing can add more details in a complex mountain region. From 1 km to 100 m, 1–12-h forecasts of temperature and humidity are also largely improved, while the wind only shows a slight improvement for 1–6-h forecasts. The influence of station network on the surface analyses is further examined. Results show that the spatial distributions of temperature and humidity at a 100-m space scale are more realistic and accurate when adding an intensive automatic weather station network, as more observational information can be absorbed. The adding of a station network can also reduce forecast errors, which can last for about 6 h. However, although surface winds display better analysis skill when more stations are added, the wind at the mountaintop region sometimes encounters a marginally worse effect for both analysis and forecast. The results are helpful to improve the analysis and forecast products in complex terrain and have some implications for downscaling from a coarse grid size to a finer grid.

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Trent W. Ford
,
Jason A. Otkin
,
Steven M. Quiring
,
Joel Lisonbee
,
Molly Woloszyn
,
Junming Wang
, and
Yafang Zhong

Abstract

Increased flash drought awareness in recent years has motivated the development of numerous indicators for monitoring, early warning, and assessment. The flash drought indicators can act as a complementary set of tools by which to inform flash drought response and management. However, the limitations of each indicator much be measured and communicated between research and practitioners to ensure effectiveness. The limitations of any flash drought indicator are better understood and overcome through assessment of indicator sensitivity and consistency; however, such assessment cannot assume any single indicator properly represents the flash drought “truth.” To better understand the current state of flash drought monitoring, this study presents an intercomparison of nine, widely used flash drought indicators. The indicators represent perspectives and processes that are known to drive flash drought, including evapotranspiration and evaporative demand, precipitation, and soil moisture. We find no single flash drought indicator consistently outperforms all others across the contiguous United States. We do find the evaporative demand- and evapotranspiration-driven indicators tend to lead precipitation- and soil moisture-based indicators in flash drought onset, but also tend to produce more flash drought events collectively. Overall, the regional and definition-specific variability in results supports the argument for a multi-indicator approach for flash drought monitoring, as advocated by recent studies. Furthermore, flash drought research—especially evaluation of historical and potential future changes in flash drought characteristics—should test multiple indicators, datasets, and methods for representing flash drought, and ideally employ a multi-indicator analysis framework over use of a single indicator from which to infer all flash drought information.

Significance Statement

Rapid onset or “flash” drought has been an increasing concern globally, with quickly intensifying impacts to agriculture, ecosystems, and water resources. Many tools and indicators have been developed to monitor and provide early warning for flash drought, ideally resulting in more time for effective mitigation and reduced impacts. However, there remains no widely accepted single method for defining, monitoring, and measuring flash drought, which means most indicators that are developed are compared with other individual indicators or conditions and impacts in one or two flash drought events. In this study, we measure the state of flash drought monitoring through an intercomparison of nine, widely used flash drought indicators that represent different aspects of flash drought. We find that no single flash drought indicator outperformed all others and suggest that a comprehensive flash drought monitor should leverage multiple, complementary indicators, datasets, and methods. Furthermore, we suggest flash drought research—especially that which reflects on historical or projected changes in flash drought characteristics—should seek multiple indicators, datasets, and methods for analyses, thereby reducing the potentially confounding effects of sensitivity to a single indicator.

Open access
Martin Ridal
,
Jana Sanchez-Arriola
, and
Mats Dahlbom

Abstract

The use of radial velocity information from the European weather radar network is a challenging task, because of a heterogeneous radar network and the different ways of providing the Doppler velocity information. Preprocessing is therefore needed to harmonize the data. Radar observations consist of a very high resolution dataset, which means that it is both demanding to process as well as that the inherent resolution is much higher than the model resolution. One way of reducing the number of data is to create “super observations” (SO) by averaging observations in a predefined area. This paper describes the preprocessing necessary to use radar radial velocities in the data assimilation where the SO construction is included. Our main focus is to optimize the use of radial velocities in the HARMONIE–AROME numerical weather model. Several experiments were run to find the best settings for first-guess check limits as well as a tuning of the observation error value. The optimal size of the SO and the corresponding thinning distance for radar radial velocities was also studied. It was found that the radial velocity information and the reflectivity from weather radars can be treated differently when it comes to the size of the SO and the thinning. A positive impact was found when adding the velocities together with the reflectivity using the same SO size and thinning distance, but the best results were found when the SO and thinning distance for the radial velocities are smaller than the corresponding values for reflectivity.

Open access