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Dev Niyogi, Sajad Jamshidi, David Smith, and Olivia Kellner

Abstract

An intercomparison of multiresolution evapotranspiration (ET) datasets with reference to ground-based measurements for the development of regional reference (ETref) and actual (ETa) evapotranspiration maps over Indiana is presented. A representative ETref equation for the state is identified by evaluating 10 years of in situ measurements (2009–19). A statewide ETref climatology is developed using the ETref equation and high-resolution surface meteorological data from the gridded surface meteorological dataset (gridMET). For ETa analyses, MODIS, Simplified Surface Energy Balance Operational dataset (SSEBop), Global Land Evaporation Amsterdam Model (GLEAM) (versions 3.3a and 3.3b), and NLDAS (Noah and VIC) datasets are evaluated using AmeriFlux data. Thirty years of rainfall data from Climate Hazards Group Infrared Precipitation with Station Data Rainfall (CHIRPS) are used with the ET datasets to develop effective precipitation fields. Results show that the standardized Penman–Monteith equation performs as the best ETref equation with median symmetric accuracy (MSA) of 0.37, Taylor’s skill score (TSC) of 0.89, and r 2 = 0.83. The analysis shows that the gridMET dataset overestimates wind speed and requires adjustment before a series of statewide ETref climatology maps are generated (1990–2020). For ETa, the MODIS and GLEAM (3.3b) datasets outperform the rest, with MSA = 0.5, TSC = 0.8, and r 2 = 0.8. The state ETa dataset is generated using all MODIS data from 2003 and blending the MODIS data with GLEAM (3.3b) to cover data unavailability. Using the top-performing datasets, annual ETref for Indiana is computed as 1110 mm, ETa as 708 mm, and precipitation as 1091 mm. A marginal increasing climatological trend is found for Indiana’s ETref (0.013 mm yr−1) while ETa is found to be relatively stable. The state’s water availability, defined as rainfall minus ETa, has remained positive and stable at 0.99 mm day−1 (annual magnitude of +3820 mm).

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Yingkai Sha, David John Gagne II, Gregory West, and Roland Stull

Abstract

Many statistical downscaling methods require observational inputs and expert knowledge and thus cannot be generalized well across different regions. Convolutional neural networks (CNNs) are deep-learning models that have generalization abilities for various applications. In this research, we modify UNet, a semantic-segmentation CNN, and apply it to the downscaling of daily maximum/minimum 2-m temperature (TMAX/TMIN) over the western continental United States from 0.25° to 4-km grid spacings. We select high-resolution (HR) elevation, low-resolution (LR) elevation, and LR TMAX/TMIN as inputs; train UNet using Parameter–Elevation Regressions on Independent Slopes Model (PRISM) data over the south- and central-western United States from 2015 to 2018; and test it independently over both the training domains and the northwestern United States from 2018 to 2019. We found that the original UNet cannot generate enough fine-grained spatial details when transferred to the new northwestern U.S. domain. In response, we modified the original UNet by assigning an extra HR elevation output branch/loss function and training the modified UNet to reproduce both the supervised HR TMAX/TMIN and the unsupervised HR elevation. This improvement is named “UNet-Autoencoder (AE).” UNet-AE supports semisupervised model fine-tuning for unseen domains and showed better gridpoint-level performance with more than 10% mean absolute error (MAE) reduction relative to the original UNet. On the basis of its performance relative to the 4-km PRISM, UNet-AE is a good option to provide generalizable downscaling for regions that are underrepresented by observations.

Open access
Yingkai Sha, David John Gagne II, Gregory West, and Roland Stull

Abstract

Statistical downscaling (SD) derives localized information from larger-scale numerical models. Convolutional neural networks (CNNs) have learning and generalization abilities that can enhance the downscaling of gridded data (Part I of this study experimented with 2-m temperature). In this research, we adapt a semantic-segmentation CNN, called UNet, to the downscaling of daily precipitation in western North America, from the low resolution (LR) of 0.25° to the high resolution (HR) of 4-km grid spacings. We select LR precipitation, HR precipitation climatology, and elevation as inputs; train UNet over the subset of the south- and central-western United States using Parameter–Elevation Regressions on Independent Slopes Model (PRISM) data from 2015 to 2018, and test it independently in all available domains from 2018 to 2019. We proposed an improved version of UNet, which we call Nest-UNet, by adding deep-layer aggregation and nested skip connections. Both the original UNet and Nest-UNet show generalization ability across different regions and outperform the SD baseline (bias-correction spatial disaggregation), with lower downscaling error and more accurate fine-grained textures. Nest-UNet also shares the highest amount of information with station observations and PRISM, indicating good ability to reduce the uncertainty of HR downscaling targets.

Open access
S. C. Pryor, F.W. Letson, and R. J. Barthelmie

Abstract

ERA5 provides high-resolution, high-quality hourly wind speeds at 100 m and is a unique resource for quantifying temporal variability in likely wind-derived power production across the United States. Gross capacity factors (CF) in seven independent system operators (ISOs) are estimated using the location and rated power of each wind turbine, a simplified power curve, and ERA5 output from 1979 to 2018. Excluding the California ISO, the marginal probability of a calm (zero power production) is less than 0.1 in any ERA5 grid cell. When a calm occurs, the mean co-occurrence across wind-turbine-containing grid cells ranges from 0.38 to 0.39 for ISOs in the Midwest and central plains [Midcontinent (or Midwest) ISO (MISO), Southwest Power Pool (SPP), and the Electric Reliability Council of Texas (ERCOT) region], increasing to 0.54–0.58 for ISOs in the eastern United States [Pennsylvania–New Jersey–Maryland interconnection (PJM), New York ISO (NYISO), and New England ISO (NEISO)]. Periods with low gross CF have a median duration of ≤6 h, except in California, and are most likely during summer. Gross CF exhibit highest variance at periods of 1 day in ERCOT and SPP; on synoptic scales in MISO, NEISO, and NYISO; and on interannual time scales in PJM. This implies differences in optimal strategies for ensuring resilience of supply. Theoretical scenarios show adding wind energy capacity near existing wind farms is advantageous even in areas with high existing installed capacity (IC), while expanding into areas with lower IC is more beneficial to reducing ramps and the probability of gross CF falling below 20%. These results emphasize the benefits of large balancing areas and aggregation in reducing wind power variability and the likelihood of wind droughts.

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Niilo Siljamo, Otto Hyvärinen, Aku Riihelä, and Markku Suomalainen

Abstract

Snow cover plays a significant role in the weather and climate system by affecting the energy and mass transfer between the surface and the atmosphere. It also has far-reaching effects on ecosystems of snow-covered areas. Therefore, global snow-cover observations in a timely manner are needed. Satellite-based instruments can be utilized to produce snow-cover information that is suitable for these needs. Highly variable surface and snow-cover features suggest that operational snow extent algorithms may benefit from at least a partly empirical approach that is based on carefully analyzed training data. Here, a new two-phase snow-cover algorithm utilizing data from the Advanced Very High Resolution Radiometer (AVHRR) on board the MetOp satellites of the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) is introduced and evaluated. This algorithm is used to produce the MetOp/AVHRR H32 snow extent product for the Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). The algorithm aims at direct detection of snow-covered and snow-free pixels without preceding cloud masking. Pixels that cannot be classified reliably to snow or snow-free, because of clouds or other reasons, are set as unclassified. This reduces the coverage but increases the accuracy of the algorithm. More than four years of snow-depth and state-of-the-ground observations from weather stations were used to validate the product. Validation results show that the algorithm produces high-quality snow coverage data that may be suitable for numerical weather prediction, hydrological modeling, and other applications.

Open access
Liang Wang, Dan Li, Ning Zhang, Jianning Sun, and Weidong Guo

Abstract

Urban heat islands (UHIs) are caused by a multitude of changes induced by urbanization. However, the relative importance of biophysical and atmospheric factors in controlling the UHI intensity remains elusive. In this study, we quantify the magnitude of surface UHIs (SUHIs), or surface urban cool islands (SUCIs), and elucidate their biophysical and atmospheric drivers on the basis of observational data collected from one urban site and two rural grassland sites in and near the city of Nanjing, China. Results show that during the daytime a strong SUCI effect is observed when the short grassland site is used as the reference site whereas a moderate SUHI effect is observed when the tall grassland is used as the reference site. We find that the former is mostly caused by the lower aerodynamic resistance for convective heat transfer at the urban site and the latter is primarily caused by the higher surface resistance for evapotranspiration at the urban site. At night, SUHIs are observed when either the short or the tall grassland site is used as the reference site and are predominantly caused by the stronger release of heat storage at the urban site. In general, the magnitude of SUHI is much weaker, and even becomes SUCI during daytime, with the short grassland site being the reference site because of its larger aerodynamic resistance. The study highlights that the magnitude of SUHIs and SUCIs is mostly controlled by urban–rural differences of biophysical factors, with urban–rural differences of atmospheric conditions playing a minor role.

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Christina Kumler-Bonfanti, Jebb Stewart, David Hall, and Mark Govett

Abstract

Extracting valuable information from large sets of diverse meteorological data is a time-intensive process. Machine-learning methods can help to improve both speed and accuracy of this process. Specifically, deep-learning image-segmentation models using the U-Net structure perform faster and can identify areas that are missed by more restrictive approaches, such as expert hand-labeling and a priori heuristic methods. This paper discusses four different state-of-the-art U-Net models designed for detection of tropical and extratropical cyclone regions of interest (ROI) from two separate input sources: total precipitable water output from the Global Forecast System (GFS) model and water vapor radiance images from the Geostationary Operational Environmental Satellite (GOES). These models are referred to as International Best Track Archive for Climate Stewardship (IBTrACS)-GFS, Heuristic-GFS, IBTrACS-GOES, and Heuristic-GOES. All four U-Nets are fast information extraction tools and perform with an ROI detection accuracy ranging from 80% to 99%. These are additionally evaluated with the Dice and Tversky intersection-over-union (IoU) metrics, having Dice coefficient scores ranging from 0.51 to 0.76 and Tversky coefficients ranging from 0.56 to 0.74. The extratropical cyclone U-Net model performed 3 times as fast as the comparable heuristic model used to detect the same ROI. The U-Nets were specifically selected for their capabilities in detecting cyclone ROI beyond the scope of the training labels. These machine-learning models identified more ambiguous and active ROI missed by the heuristic model and hand-labeling methods that are commonly used in generating real-time weather alerts, having a potentially direct impact on public safety.

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R. Bassett, P. J. Young, G. S. Blair, F. Samreen, and W. Simm

Abstract

Lagos, Nigeria, is rapidly urbanizing and is one of the fastest-growing cities in the world, with a population that is increasing at almost 500 000 people per year. Yet the impacts on Lagos’s local climate via its urban heat island (UHI) have not been well explored. Considering that the tropics already have year-round high temperatures and humidity, small changes are very likely to tip these regions over heat-health thresholds. Using a well-established model, but with an extended investigation of uncertainty, we explore the impact of Lagos’s recent urbanization on its UHI. Following a multiphysics evaluation, our simulations, against the background of an unusually warm period in February 2016 (during which temperatures regularly exceeded 36°C), show a 0.44°C ensemble-time-mean increase in nighttime UHI intensity between 1984 and 2016. The true scale of the impact is seen spatially as the area over which ensemble-time-mean UHIs exceed 1°C was found to increase steeply from 254 km2 in 1984 to 1572 km2 in 2016. The rate of warming within Lagos will undoubtedly have a high impact because of the size of the population (12+ million) already at risk from excess heat. Significant warming and modifications to atmospheric boundary layer heights are also found in rural areas downwind, directly caused by the city. However, there is limited long-term climate monitoring in Lagos or many similarly expanding cities, particularly in the tropics. As such, our modeling can only be an indication of this impact of urbanization, and we highlight the urgent need to deploy instrumentation.

Open access
Federico Flores, Andrés Arriagada, Nicolás Donoso, Andrés Martínez, Aldo Viscarra, Mark Falvey, and Rainer Schmitz

Abstract

In desert environments, intense radiative cooling of the surface during the night leads to rapid cooling of the adjacent air, resulting in a strong temperature inversion conducive to cold-air-pool formation. In this work observations are analyzed to investigate the structure of a nocturnal cold-air pool inside a semiclosed basin located near Sierra Gorda in the Atacama Desert in Chile and its effect on dust dispersion in the area. The measurement campaign was conducted over a 5-day period (14–19 August) in 2017 and included ceilometer data, vertical profiles of temperature, a grid of fixed ground stations, and mobile temperature sensors. We focus our attention on the conditions during periods of high levels of dust pollution, in order to understand the atmospheric conditions that contribute to these episodes. The analysis of the available data confirms the development of an intense nocturnal cold-air pool, which is reflected in a strong nocturnal potential temperature inversion (18 K in 150 m) and a 30°C diurnal temperature range. A comparison of the vertical distribution of dust and temperature shows that the capping inversion controls the location of the dust cloud. As a consequence, the highest dust concentrations were observed inside the cold pool, below the capping inversion, proving that within the basin the dust is confined to the layer where its source is located.

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Sarah M. Borg, Steven M. Cavallo, and David D. Turner

Abstract

Tropopause polar vortices (TPVs) are long-lived, coherent vortices that are based on the dynamic tropopause and characterized by potential vorticity anomalies. TPVs exist primarily in the Arctic, with potential impacts ranging from surface cyclone generation and Rossby wave interactions to dynamic changes in sea ice. Previous analyses have focused on model output indicating the importance of clear-sky and cloud-top radiative cooling in the maintenance and evolution of TPVs, but no studies have focused on local observations to confirm or deny these results. This study uses cloud and atmospheric state observations from Summit Station, Greenland, combined with single-column experiments using the Rapid Radiative Transfer Model to investigate the effects of clear-sky, ice-only, and all-sky radiative cooling on TPV intensification. The ground-based observing system combined with temperature and humidity profiles from the European Centre for Medium-Range Weather Forecasts’s fifth major global reanalysis dataset, which assimilates the twice-daily soundings launched at Summit, provides novel details of local characteristics of TPVs. Longwave radiative contributions to TPV diabatic intensity changes are analyzed with these resources, starting with a case study focusing on observed cloud properties and associated radiative effects, followed by a composite study used to evaluate observed results alongside previously simulated results. Stronger versus weaker vertical gradients in anomalous clear-sky radiative heating rates, contributing to Ertel potential vorticity changes, are associated with strengthening versus weakening TPVs. Results show that clouds are sometimes influential in the intensification of a TPV, and composite results share many similarities to modeling studies in terms of atmospheric state and radiative structure.

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