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R. A. Wakefield
,
D. D. Turner
,
T. Rosenberger
,
T. Heus
,
T. J. Wagner
,
J. Santanello
, and
J. Basara

Abstract

Land–atmosphere interactions play a critical role in both the atmospheric water and energy cycles. Changes in soil moisture and vegetation alter the partitioning of surface water and energy fluxes, influencing diurnal evolution of the planetary boundary layer (PBL). The mixing-diagram framework has proven useful in understanding the evolution of the heat and moisture budget within the convective boundary layer (CBL). We demonstrate that observations from the Department of Energy Atmospheric Radiation Measurement (ARM) Southern Great Plains site provide all of the needed inputs needed for the mixing-diagram framework, allowing us to quantify the impact from the surface fluxes, advection, radiative heating, encroachment, and entrainment on the evolution of the CBL. Profiles of temperature and humidity retrieved from the ground-based infrared spectrometer [Atmospheric Emitted Radiance Interferometer (AERI)] are a critical component in this analysis. Large-eddy simulation results demonstrate that mean mixed-layer values derived are shown to be critical to close the energy and moisture budgets. A novel approach demonstrated here is the use of network of AERIs and Doppler lidars to quantify the advective fluxes of heat and moisture. The framework enables the estimation of the entrainment fluxes as a residual, providing a way to observe the entrainment fluxes without using multiple lidar systems. The high temporal resolution of the AERI observations enables the morning, midday, and afternoon evolution of the CBL to be quantified. This work provides a new way to use observations in this framework to evaluate weather and climate models.

Significance Statement

The energy and moisture budget of the planetary boundary layer (PBL) is influenced by multiple sources, and accurately representing this evolution in numerical models is critical for weather forecasts and climate predictions. The mixing-diagram approach, driven by profiling observations as illustrated here, provides a powerful way to quantify the contributions from each of these sources. In particular, the energy and moisture mixed into the PBL from above the PBL can be determined accurately from ground-based remote sensors using this approach.

Open access
Windmanagda Sawadogo
,
Jan Bliefernicht
,
Benjamin Fersch
,
Seyni Salack
,
Samuel Guug
,
Kehinde O. Ogunjobi
,
Stefanie Meilinger
, and
Harald Kunstmann

Abstract

The number of solar power plants has increased in West Africa in recent years. Reliable reanalysis data and short-term forecasting of solar irradiance from numerical weather prediction models could provide an economic advantage for the planning and operation of solar power plants, especially in data-poor regions such as West Africa. This study presents a detailed assessment of different shortwave (SW) radiation schemes from the Weather Research and Forecasting (WRF) Model option Solar (WRF-Solar), with appropriate configurations for different atmospheric conditions in Ghana and the southern part of Burkina Faso. We applied two 1-way nested domains (D1 = 15 km and D2 = 3 km) to investigate four different SW schemes, namely, the Community Atmosphere Model, Dudhia, RRTMG, Goddard, and RRTMG without aerosol and with aerosol inputs (RRTMG_AERO). The simulation results were validated using hourly measurements from different automatic weather stations established in the study region in recent years. The results show that the RRTMG_AERO_D01 generally outperforms the other SW radiation schemes to simulate global horizontal irradiance under all-sky condition [RMSE = 235 W m−2 (19%); MAE = 172 W m−2 (14%)] and also under cloudy skies. Moreover, RRTMG_AERO_D01 shows the best performance on a seasonal scale. Both the RRTMG_AERO and Dudhia experiments indicate a good performance under clear skies. However, the sensitivity study of different SW radiation schemes in the WRF-Solar model suggests that RRTMG_AERO gives better results. Therefore, it is recommended that it be used for solar irradiance forecasts over Ghana and the southern part of Burkina Faso.

Open access
Amy Clement
,
Tiffany Troxler
,
Oaklin Keefe
,
Marybeth Arcodia
,
Mayra Cruz
,
Alyssa Hernandez
,
Diana Moanga
,
Zelalem Adefris
,
Natalia Brown
, and
Susan Jacobson

Abstract

Cities around the world are experiencing the effects of climate change via increasing extreme heat worsened by urbanization. Within cities, there are disparities in extreme heat exposure that are apparent in various surface and remotely sensed observations, as well as in the health impacts. There are, however, large data gaps in our ability to quantify the heat experienced by people in their daily lives across urban areas. In this paper, we use hyperlocal observations to measure heat around Miami–Dade County, Florida. Temperature and humidity measurements were collected at sites throughout the county between 2018 and 2021 with low-cost sensors. By comparing these hyperlocal observations with a National Weather Service (NWS) site at the Miami International Airport (MIA), we show that maximum temperatures are on average 6°F (3.3°C) higher and maximum heat index values are 11°F (6.1°C) higher at sites in the county than at MIA. These measurements show that many sites frequently record a heat index above the local threshold value for heat advisory. This is in contrast with the fact that few forecast advisories are issued, and there are correspondingly few exceedances of the threshold at MIA. We use these results to motivate a discussion about the issues of this particular threshold for Miami–Dade County. We highlight the need for data that are closer to residents’ lived experience to assess the impacts of heat and help inform local and regional decision-making, particularly where heat exposure may be underappreciated as a potential public health hazard.

Open access
Fong Ngan
,
Christopher P. Loughner
,
Sonny Zinn
,
Mark Cohen
,
Temple R. Lee
,
Edward Dumas
,
Travis J. Schuyler
,
C. Bruce Baker
,
Joseph Maloney
,
David Hotz
, and
George Mathews

Abstract

A series of meteorological measurements with a small uncrewed aircraft system (sUAS) was collected at Oliver Springs Airport in Tennessee. The sUAS provides a unique observing system capable of obtaining vertical profiles of meteorological data within the lowest few hundred meters of the boundary layer. The measurements benefit simulated plume predictions by providing more accurate meteorological data to a dispersion model. The sUAS profiles can be used directly to drive HYSPLIT dispersion simulations. When using sUAS data covering a small domain near a release and meteorological model fields covering a larger domain, simulated pollutants may be artificially increased or decreased near the domain boundary because of inconsistencies in the wind fields between the two meteorological inputs. Numerical experiments using the Weather Research and Forecasting (WRF) Model with observational nudging reveal that incorporating sUAS data improves simulated wind fields and can significantly affect mixing characteristics of the boundary layer, especially during the morning transition period of the planetary boundary layer. We conducted HYSPLIT dispersion simulations for hypothetical releases for three case study periods using WRF meteorological fields with and without assimilating sUAS measurements. The comparison of dispersion results on 15 and 16 December 2021 shows that using sUAS observational nudging is more significant under weak synoptic conditions than under strong influences from regional weather. Very different dispersion results were introduced by the meteorological fields used. The observational nudging produced not just an sUAS-nudged wind flow but also adjusted meteorological fields that further impacted the mixing calculation in HYSPLIT.

Open access
Alfonso Hernanz
,
Carlos Correa
,
Marta Domínguez
,
Esteban Rodríguez-Guisado
, and
Ernesto Rodríguez-Camino

Abstract

Statistical downscaling (SD) of climate change projections is a key piece for impact and adaptation studies due to its low computational expense compared to dynamical downscaling, which allows exploration of uncertainties through the generation of large ensembles. SD has been extensively evaluated and applied in the extratropics, but few examples exist in tropical regions. In this study, several state-of-the-art methods belonging to different families have been evaluated for maximum/minimum daily temperature and daily accumulated precipitation (both from the ERA5 at 0.25°) in two regions with very different climates: Spain (midlatitudes) and Central America (tropics). Some key assumptions of SD have been tested: the strength of the predictor–predictand links, the skill of different approaches, and the extrapolation capability of each method. It has been found that relevant predictors are different in both regions, as is the behavior of statistical methods. For temperature, most methods perform significantly better in Spain than in Central America, where transfer function (TF) methods present important extrapolation problems, probably due to the low variability of the training sample (present climate). In both regions, model output statistics (MOS) methods have achieved the best results for temperature. In Central America, TF methods have achieved better results than MOS methods in the evaluation in the present climate, but they do not preserve trends in the future. For precipitation, MOS methods and the extreme gradient boost machine learning method have achieved the best results in both regions. In addition, it has been found that, although the use of humidity indices as predictors improves results for the downscaling of precipitation, future trends given by statistical methods are very sensitive to the use of one or another index. Three indices have been compared: relative humidity, specific humidity, and dewpoint depression. The use of the specific humidity has been found to lead to trends given by the downscaled projections that deviate seriously from those given by raw global climate models in both regions.

Open access
Anamika Shreevastava
,
Colin Raymond
, and
Glynn C. Hulley

Abstract

Heatwaves in California manifest as both dry and humid events. While both forms have become more prevalent, recent studies have identified a shift toward more humid events. Understanding the complex interactions of each heatwave type with the urban heat island is crucial for impacts but remains understudied. Here, we address this gap by contrasting how dry versus humid heatwaves shape the intraurban heat of the greater Los Angeles area. We used a consecutive contrasting set of heatwaves from 2020 as a case study: a prolonged humid heatwave in August and an extremely dry heatwave in September. We used MERRA-2 reanalysis data to compare mesoscale dynamics, followed by high-resolution Weather Research and Forecasting modeling over urbanized Southern California. We employ moist thermodynamic variables to quantify heat stress and perform spatial clustering analysis to characterize the spatiotemporal intraurban variability. We find that, despite temperatures being 10° ± 3°C hotter in the September heatwave, the wet-bulb temperature, closely related to the risk of human heat stroke, was higher in August. While dry and humid heat display different spatial patterns, three distinct spatial clusters emerge based on nonheatwave local climates. Both types of heatwaves diminish the intraurban heat stress variability. Valley areas such as San Bernardino and Riverside experience the worst impacts, with up to 6° ± 0.5°C of additional heat stress during heatwave nights. Our results highlight the need to account for the disparity in small-scale heatwave patterns across urban neighborhoods in designing policies for equitable climate action.

Significance Statement

Heatwaves are the leading cause of morbidity and mortality among all environmental hazards. Moreover, their frequency and intensity are on the rise due to climate change. Southern California is no stranger to extreme heat, but persistently humid heatwaves still test the adaptability limits of its residents. We find that the set of two contrasting heatwaves that afflicted Los Angeles in the summer of 2020 forms the perfect testbed for characterizing the impacts of humid versus dry heatwaves on urban environment. Because climate model forecasts and long-term observational trends point to more humid heatwaves in the future for Southern California, our results underscore the importance of including moist heat in extreme heat warning frameworks.

Open access
Chuancheng Zhao
,
Shuxia Yao
,
Yongjian Ding
, and
Qiudong Zhao

Abstract

An accurate and reliable precipitation product on regular grids is essential for understanding trends and variability within climate studies, for weather forecasting, and in hydrology and agrometeorology applications. However, the construction of high-resolution spatiotemporal precipitation grid products is challenging for complex terrain with sparse rain gauge networks and when only coarse spatial resolutions of satellite data are available. The objective of this study was to consequently provide a practical method to create a grid precipitation product by merging accurate quantitative observations from weather stations with continuous spatial information and from a satellite-based estimate product. The new gridded precipitation product exhibits a monthly temporal resolution and a spatial resolution of 0.01° for the Tian Shan range, extending back to 1981. To overcome the limitation of low densities and sparse distributions of meteorological stations in the complex terrain of the Tian Shan, a suitable interpolation of Australian National University Spline (ANUSPLIN) was used to interpolate grid precipitation based on in situ data. The interpolation grid precipitation was then merged with the satellite precipitation product developed by the U.S. Geological Survey and the Climate Hazards Group. After evaluation and validation using withheld stations and comparison to reference datasets, the result indicated that the merged product exhibits considerable promise for application in complex terrain. The method can be widely applied and is expected to construct precipitation products with high spatial and temporal resolution by merging multiple precipitation data sources.

Significance Statement

The purpose of this study is to construct a gridded precipitation dataset by merging the interpolation precipitation based on in situ observation and a satellite precipitation product in arid mountain regions. This is important because gridded precipitations are essential to the evaluation of climate model output and detection of trends in mean climate and climate extremes. Our results present a guide on merging frameworks to construct precipitation datasets used multisource data sources for regions with complex topography, precipitation scarcity, and ungauged and sparse collection.

Open access
Dilchand Nauth
,
Christopher P. Loughner
, and
Maria Tzortziou

Abstract

The continually changing atmospheric conditions over densely populated coastal urban regions make it challenging to produce models that accurately capture the complex interactions of anthropogenic and environmental emissions, chemical reactions, and unique meteorological processes, such as sea- and land-breeze circulations. The purpose of this study is to determine and identify the influence of synoptic-scale wind patterns on the development of local-scale sea-breeze circulations and air quality over the New York City (NYC), New York, metropolitan area. This study utilizes column-integrated nitrogen dioxide observations made during the Long Island Sound Tropospheric Ozone Study (LISTOS) field campaign, ground-level ozone observations, the HRRR numerical weather prediction model, and trajectory model simulations using the NOAA HYSPLIT model. A cluster analysis within the HYSPLIT modeling system was performed to determine that there were six unique synoptic-scale transport pathways for NYC. Stagnant conditions or weak transport out of the northwest resulted in the worst air quality for NYC. Weak synoptic-scale forcings associated with these conditions allowed for local-scale sea-breeze circulations to develop, resulting in air pollution being able to recirculate and mix with freshly emitted pollutants.

Significance Statement

The purpose of this work is to understand how synoptic-scale wind patterns influence air quality and sea-breeze circulations in the New York City, New York, metropolitan area. This work shows that clean air can be imported into the region from rural New England and over the Atlantic Ocean, whereas polluted air can be transported into the region from the northwest and southwest. This work also shows the importance of the strength in synoptic-scale forcings in the development of sea-breeze circulations. Weak synoptic-scale winds allow for strong sea-breeze circulations to develop over all coastlines in the New York City region, resulting in air pollutants recirculating and mixing with freshly emitted air pollution and contributing to poor air quality.

Open access
Lanzhi Tang
,
Wenhua Gao
,
Lulin Xue
,
Guo Zhang
, and
Jianping Guo

Abstract

The long-term characteristics of four hydrometeor species (cloud water, cloud ice, rain, and snow) in precipitating clouds over eastern China (divided into South China, Jianghuai, and North China) and their relationships with surface rainfall are first investigated using the fifth major global reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) hourly dataset from May to August during 1979–2020. The results show that the cloud water path decreases significantly from south to north as a result of the large-scale circulation and water vapor distribution, with the maximum value of 180 g m−2 in South China and only one-half of that value in North China. The slope in linear relationship between rainwater path and precipitation intensity is at the maximum (5.68 h−1) in South China, implying the highest conversion rate from rainwater to precipitation in this region. When the precipitation rate exceeds 15 mm h−1, the ice-phase hydrometeor contents in South China become the largest among the three regions, indicating that the cold-rain process is crucial to heavy rainfall. The moisture-related processes play a dominant role in the precipitation intensity. Although the contribution of hydrometeor advection to precipitation is generally between −5% and 5%, we found that it can jointly modulate the location of heavy rainfall. In addition, the peaks of cloud water path commonly appear 2–3 h ahead of precipitation, whereas the peaks of ice-phase particles occur 2 and 1 h behind the afternoon precipitation onset in South China and Jianghuai, respectively, which is mainly attributed to the different upward velocity and water vapor convergence in the mid–upper troposphere.

Significance Statement

Reanalysis data and satellite retrievals have been widely used in investigating cloud water and cloud ice in nonprecipitating clouds. However, studies on long-term characteristics of precipitating hydrometeors in precipitating clouds, which are directly connected and crucial to surface rainfall, are still very limited to date because of limitations in observations of precipitating clouds. In this study, the latest ERA5 reanalysis hourly dataset is first used to quantitatively explore the climatological characteristics of four hydrometeors (cloud water, cloud ice, rain, and snow) in precipitating clouds as well as their relationships with precipitation intensity over eastern China from 1979 to 2020. The results advance our understanding of precipitation mechanisms from the perspective of hydrometeor climatology.

Open access
Ali Tokay
,
Liang Liao
,
Robert Meneghini
,
Charles N. Helms
,
S. Joseph Munchak
,
David B. Wolff
, and
Patrick N. Gatlin

Abstract

Parameters of the normalized gamma particle size distribution (PSD) have been retrieved from the Precipitation Image Package (PIP) snowfall observations collected during the International Collaborative Experiment–PyeongChang Olympic and Paralympic winter games (ICE-POP 2018). Two of the gamma PSD parameters, the mass-weighted particle diameter D mass and the normalized intercept parameter NW , have median values of 1.15–1.31 mm and 2.84–3.04 log(mm−1 m−3), respectively. This range arises from the choice of the relationship between the maximum versus equivalent diameter, D mxD eq, and the relationship between the Reynolds and Best numbers, Re–X. Normalization of snow water equivalent rate (SWER) and ice water content W by NW reduces the range in NW , resulting in well-fitted power-law relationships between SWER/NW and D mass and between W/NW and D mass. The bulk descriptors of snowfall are calculated from PIP observations and from the gamma PSD with values of the shape parameter μ ranging from −2 to 10. NASA’s Global Precipitation Measurement (GPM) mission, which adopted the normalized gamma PSD, assumes μ = 2 and 3 in its two separate algorithms. The mean fractional bias (MFB) of the snowfall parameters changes with μ, where the functional dependence on μ depends on the specific snowfall parameter of interest. The MFB of the total concentration was underestimated by 0.23–0.34 when μ = 2 and by 0.29–0.40 when μ = 3, whereas the MFB of SWER had a much narrower range (from −0.03 to 0.04) for the same μ values.

Free access