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Penghui Zhang
,
Shaokun Deng
,
Peng-Fei Tuo
, and
Shengli Chen

Abstract

With the rising global demand for renewable energy sources, a great number of offshore wind farms are being built worldwide, as well as in the northern South China Sea. There is, however, limited research on the impact of offshore wind farms on the atmospheric and marine environment, particularly tropical cyclones, which frequently occur in summertime in the South China Sea. In this paper, we employ the Weather Research and Forecasting (WRF) model to investigate the impacts of large-scale offshore wind farms on tropical cyclones, using the case of Typhoon Hato, which caused severe damage in 2017. Model results reveal that maximum wind speeds in coastal areas decrease by 3–5 m/s and can reach a maximum of 8 m/s. Furthermore, the wind farms change low-level moisture convergence, causing a shift of the precipitation center towards the wind farm area and causing a significant overall reduction (up to 16%) in precipitation. Model sensitivity experiments on the area and layout of the wind farm have been carried out. Results show that larger wind farm areas and denser turbine layouts cause a more substantial decrease in the wind speed over the coast and accumulated precipitation reduction, further corroborating our findings.

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Reese Mishler
,
Guifu Zhang
, and
Vivek N. Mahale

Abstract

Polarimetric variables such as differential phase ΦDP and its range derivative, specific differential phase K DP, contain useful information for improving quantitative precipitation estimation (QPE) and microphysics retrieval. However, the usefulness of the current operationally utilized estimation method of K DP is limited by measurement error and artifacts resulting from the differential backscattering phase δ. The contribution of δ can significantly influence the ΦDP measurements and therefore negatively affect the K DP estimates. Neglecting the presence of δ within non-Rayleigh scattering regimes has also led to the adoption of incorrect terminology regarding signatures seen within current operational K DP estimates implying associated regions of unrealistic liquid water content. A new processing method is proposed and developed to estimate both K DP and δ using classification and linear programming (LP) to reduce bias in K DP estimates caused by the δ component. It is shown that by applying the LP technique specifically to the rain regions of Rayleigh scattering along a radial profile, accurate estimates of differential propagation phase, specific differential phase, and differential backscattering phase can be retrieved within regions of both Rayleigh and non-Rayleigh scattering. This new estimation method is applied to cases of reported hail and tornado debris, and the LP results are compared to the operationally utilized least squares fit (LSF) estimates. The results show the potential use of the differential backscattering phase signature in the detection of hail and tornado debris.

Free access
Nicolas G. Alonso-De-Linaje
,
Andrea N. Hahmann
,
Ioanna Karagali
,
Krystallia Dimitriadou
, and
Merete Badger

Abstract

The paper aims to demonstrate how to enhance the accuracy of offshore wind resource estimation, specifically by incorporating near-surface satellite-derived wind observations into mesoscale models. We utilized the Weather Research and Forecasting (WRF) Model and applied observational nudging by integrating ASCAT data over offshore areas to achieve this. We then evaluated the accuracy of the nudged WRF Model simulations by comparing them with data from ocean oil platforms, tall masts, and a wind lidar mounted on a commercial ferry crossing the southern Baltic Sea. Our findings indicate that including satellite-derived ASCAT wind speeds through nudging enhances the correlation and reduces the error of the mesoscale simulations across all validation platforms. Moreover, it consistently outperforms the control and previously published WRF-based wind atlases. Using satellite-derived winds directly in the model simulations also solves the issue of lifting near-surface winds to wind turbine heights, which has been challenging in estimating wind resources at such heights. The comparison of the 1-yr-long simulations with and without nudging reveals intriguing differences in the sign and magnitude between the Baltic and North Seas, which vary seasonally. The pattern highlights a distinct regional pattern attributed to regional dynamics, sea surface temperature, atmospheric stability, and the number of available ASCAT samples.

Significance Statement

We aim to showcase a method for improving the precision of hub-height estimation of wind resources offshore. This involves integrating wind observations obtained from near-surface satellites into the model simulations. To assess the accuracy of the simulations, we compare the simulated winds to data gathered from multiple offshore sources, including oil platforms, tall masts, and a wind lidar installed on a commercial ferry.

Free access
Yousuke Sato
,
Moeka Kamada
,
Akihiro Hashimoto
, and
Masaru Inatsu

Abstract

This study examined future changes in the microphysical properties of surface solid precipitation over Hokkaido, Japan. A process-tracking model that predicts the mass of the hydrometeors generated by each cloud microphysical process was implemented in a meteorological model. This implementation aimed to analyze the mass fraction of hydrometeors resulting from depositional growth and the riming process to the total mass of surface solid precipitation. Results from pseudo-global warming experiments suggest two potential future changes in the characteristics of surface solid precipitation over Hokkaido. First, the rimed particles are expected to increase and be dominant over the west and northwest coast of Hokkaido, where heavy snowfall occurs primarily due to the lake effect. Second, the mass fraction from depositional growth under relatively higher temperatures is expected to increase. This increase is anticipated to be dominant over the eastern part and mountainous area of Hokkaido. Additionally, the fraction of liquid precipitation to total precipitation is expected to increase in the future. These results suggest that the microphysical properties of solid precipitation in Hokkaido are expected to be similar to those observed in the current climate over Hokuriku, the central part of Japan even in warmer climate conditions.

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Yujeong Do
,
Kyo-Sun Sunny Lim
,
Ki-Byung Kim
,
Hyeyum Hailey Shin
,
Eun-Chul Chang
, and
GyuWon Lee

Abstract

This study investigates the impact of initial/boundary conditions (IC/BCs) and horizontal resolutions on forecast for average weather conditions, focusing on low-level weather variables such as 2-m temperature (T2m), 2-m water vapor mixing ratio (Q2m), and 10-m wind speed (WS10). A Weather Research and Forecasting (WRF) model is used for regional mesoscale model simulations and large-eddy simulations (LES). The 6-h-interval forecast fields generated by the Global Forecast System of the National Centers for Environmental Prediction and the Korean Integrated Model of the Korea Meteorological Administration are utilized as IC/BCs for the regional models. Numerical experiments are performed for 24 h starting at 0000 UTC on each day in April 2021 when the average monthly wind speed was strongest during 10 years (2011-2020). Comparison of model simulations with observations obtained around the Yeong-Jong Island, where Incheon International Airport is situated, shows that the regional models capture the time series of T2m, Q2m, and WS10 more effectively than the global model forecasts. Moreover, the LES experiments with a 100-m horizontal grid spacing simulate higher Q2m and lower WS10 during the daytime compared to the 1-km WRF. This results in a deterioration of their time series correlation with the observations. Meanwhile, the 100-m LES forecasts time series of T2m over ocean stations and Q2m over land stations, as well as probability density functions of low-level weather variables, more accurately than that of the 1-km WRF. Our study also emphasizes the need for caution when comparing high-resolution model results with observation values at specific stations due to the high spatial variability in low-level meteorological fields.

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Qing Zheng
,
Wei Sun
,
Jian Li
,
Yong Feng
,
Zhiwei Heng
, and
Xingwen Jiang

Abstract

Water vapor transport is a crucial process in modeling and can contribute to errors in precipitation forecasts. To investigate the sensitivity of precipitation to the moisture advection scheme, this study introduced the two-step shape-preserving advection scheme (TSPAS), which has been proven to improve precipitation simulation over steep topography at lower resolutions, into the Southwest Center Weather Research and Forecast (WRF)-based Intelligent Numerical Grid Forecast System (SWC-WINGS) at a convection-permitting resolution. According to experiments conducted throughout the summer of 2021, the precipitation over the eastern slope of the Tibetan Plateau (TP) is highly sensitive to the moisture advection scheme. TSPAS successfully improved precipitation over the eastern slope of the TP, especially for torrential rainfall. The fractions skill score (FSS) is improved by 0.075 (27.78%) for daily precipitation with a threshold of 100 mm. Compared with the experiment with the original WRF advection scheme, the TSPAS reduced the overestimation of precipitation in the topographic region and excessive water vapor transport in a low-level atmosphere. To understand the precipitation improvement contributed by the advection scheme, additional experiments were conducted for a particular precipitation process from two approaches: switching advection schemes during the rainfall evolution and updating the variables related to moisture advection individually. Results demonstrate that the precipitation improvement is mainly contributed by the moisture advection scheme before the precipitation. Among the different variables, the combination of wind and water vapor was the most influential factor causing the precipitation improvement under the TSPAS.

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Rui Zhao
,
Xiong Zhou
,
Jing Liu
,
Yongping Li
, and
Guohe Huang

Abstract

Significant increases in temperature and precipitation due to global warming affect socio-economics. Accurate analysis is needed for future temperature and precipitation changes in the Yangtze River Basin (YRB). A novel quantile delta-mapped spatial disaggregation (QDMSD) approach was developed in this study to analyze temperature and precipitation changes for the first time over the YRB. The evaluation results show that while QDMSD has a similar performance in simulating temperature with the bias correction and spatial disaggregation (BCSD) model, it shows improvement in reproducing precipitation. Projections indicate the annual mean temperature will increase from 2020 to 2080 under SSP2-4.5 and SSP5-8.5. The projected temperature obtained from five downscaled GCMs has the smallest ranges of differences in summer. Conversely, under SSP2-4.5, annual mean temperatures significantly decrease from 2081 to 2100. In terms of spatial distribution characteristics, most of the positive changes tend to expand across the YRB. The annual mean precipitation will increase from 2020 to 2080 but decreases from 2081 to 2100 under SSP2-4.5 over the YRB. In terms of spatial distribution, precipitation in the southeast region of the YRB will increase, and the maximum variations in precipitation will occur downstream of the YRB. The QDMSD method reproduces observed precipitation trends well and enhances simulation accuracy in the YRB. For projected temperature, there will be a widespread increase across the YRB; for projected precipitation, significant increases will occur in the eastern YRB. These findings support policy-making to address potential risks from temperature and precipitation changes across multiple sectors (e.g., agriculture and industry).

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Brian D. Hirth
and
John L. Schroeder

Abstract

A new methodology for standardizing radar-derived elevated dual-Doppler (DD) synthesized wind maps to the near surface is presented, leveraging the spatial variability found within the horizontal wind speed fields. The methodology is applied to a dataset collected by Texas Tech University (TTU) using two TTU Ka-band mobile radar systems during the landfall of Hurricane Delta (2020) in coastal Louisiana. Relevant portions of the DD wind fields are extracted from multiple heights between 100 – 400 m above ground level, combined into 10-minute segments and standardized to a reference height of 10 m and an open exposure roughness length of 0.03 m. Extractions from these standardized wind fields are compared and validated against the standardized wind measurements from a micronet of seven TTU StickNet platforms providing “ground truth” within the DD analysis domain. The validation efforts confirm the developed DD wind field standardization methodology yields robust results with correlations coefficients greater than 0.88 and mean biases less than 1%. The results of this study provide a new means for incorporating elevated DD radar data into new and existing surface wind field analysis systems geared toward generating a wind field of record during a hurricane landfall.

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Benjamin Davis
,
Elinor R. Martin
, and
Bradley G. Illston

Abstract

Extreme heat like that seen in the US and Europe in summer 2022 can have significant impacts on human health and infrastructure. The Occupational Safety and Health Administration (OSHA) and the US Army use Wet Bulb Globe Temperature (WBGT) to quantify the impact of heat on workers and inform decisions on workload. WBGT is a weighted average of air temperature, natural wet bulb temperature, and black globe temperature. A local hourly, daily, and monthly WBGT climatology will allow those planning outdoor work to minimize the likelihood of heat related disruptions. In this study, WBGT is calculated from the ERA5 reanalysis and is validated by the Oklahoma Mesonet and found to be adequate. Two common methods of calculating WBGT from meteorological observations are compared. The Liljegren method has a larger diurnal cycle than the Dimiceli method, with peak WBGT about 1 °F higher. The high and extreme risk categories in the southern United States Great Plains (USGP) have increased from 5 days per year to 15 days from 1960–2020. Additionally, the largest increases in WBGT are occurring during DJF, potentially lengthening the warm season in the future. Heat wave definitions based on maximum, minimum, and mean WBGT are used to calculate heat wave characteristics and trends with the largest number of heat waves occurring in the southern USGP. Further, the number of heat waves is generally increasing across the domain. This study shows that heat wave days based on minimum WBGT have increased significantly which could have important impacts on human heat stress recovery.

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Jacob T. Carlin
,
Elizabeth N. Smith
, and
Katherine Giannakopoulos

Abstract

Knowledge about the depth of the planetary boundary layer (PBL) is crucial for a variety of applications, but direct observations of PBL depth are spatiotemporally sparse. Recent studies have proposed using operational dual-polarization weather radars to observe the evolution of PBL depth by capitalizing on unique differential reflectivity (Z DR) signatures of Bragg scatter at the top of the PBL. While this approach appears promising and cost-effective, uncertainties remain about the representativeness of these estimates and how its efficacy may vary by geography and climatology. To address these outstanding uncertainties, this study compares collocated observations collected from two WSR-88D radars and two state-of-the-art mobile boundary layer profiling systems and evaluates the proposed methodology over the full diurnal cycle. Results indicate good overall correspondence between the profiling- and radar-based PBL depth estimates, with an abrupt divergence during the early evening transition and large discrepancies overnight. Relatively large root-mean-square-deviations (RMSDs) coupled with small biases match expectations when comparing spatially averaged data with point observations during PBL growth, which capture frequent fluctuations. A qualitative examination of the radar data reveals signatures of elevated residual layers, clouds, and ground clutter, all of which can obfuscate the desired surface-based PBL signal but which may have their own utility. The prominence of the Bragg scatter signal is found to be correlated with the observed moisture gradient at the top of the PBL, reflecting climatological variability that should be considered. These findings motivate further work to improve the automated detection of Bragg scatter layers from polarimetric radar data.

Significance Statement

Knowledge of the height of the planetary boundary layer matters for weather forecasting, air quality, and renewable energy production. Currently, boundary layer height measurements are taken at select locations twice a day. However, a method to use the existing national network of polarimetric weather radars for this purpose has been proposed. This work evaluates this method against specialized boundary layer measurements. The results show that the method is generally reliable during the daytime and could be used for a variety of applications including climatologies and model evaluation. There remain a number of situational caveats, including residual turbulence, clouds/precipitation, ground clutter, and certain meteorological environments, that may require modification of the approach and need to be considered in future work.

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