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Janel Hanrahan, Jessica Langlois, Lauren Cornell, Huanping Huang, Jonathan M. Winter, Patrick J. Clemins, Brian Beckage, and Cindy Bruyère

Abstract

Most inland water bodies are not resolved by general circulation models, requiring that lake surface temperatures be estimated. Given the large spatial and temporal variability of the surface temperatures of the North American Great Lakes, such estimations can introduce errors when used as lower boundary conditions for dynamical downscaling. Lake surface temperatures (LSTs) influence moisture and heat fluxes, thus impacting precipitation within the immediate region and potentially in regions downwind of the lakes. For this study, the Advanced Research version of the Weather Research and Forecasting Model (WRF-ARW) was used to simulate precipitation over the six New England states during a 5-yr historical period. The model simulation was repeated with perturbed LSTs, ranging from 10°C below to 10°C above baseline values obtained from reanalysis data, to determine whether the inclusion of erroneous LST values has an impact on simulated precipitation and synoptic-scale features. Results show that simulated precipitation in New England is statistically correlated with LST perturbations, but this region falls on a wet–dry line of a larger bimodal distribution. Wetter conditions occur to the north and drier conditions occur to the south with increasing LSTs, particularly during the warm season. The precipitation differences coincide with large-scale anomalous temperature, pressure, and moisture patterns. Care must therefore be taken to ensure reasonably accurate Great Lakes surface temperatures when simulating precipitation, especially in southeastern Canada, Maine, and the mid-Atlantic region.

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Yang Shi, Jiahua Wei, Yan Ren, Zhen Qiao, Qiong Li, Xiaomei Zhu, Beiming Kang, Peichong Pan, Jiongwei Cao, Jun Qiu, Tiejian Li, and Guangqian Wang

Abstract

Acoustic agglomerations have increasingly attracted widespread attention as a cost-effective and environmentally friendly approach for fog removal and weather modification. In this study, research on precipitation interference and the agglomeration performance of droplet aerosols under large-scale acoustic waves was presented. In total, 49 field experiments in the source region of the Yellow River in the summer of 2019 were performed to reveal the influences of acoustic waves on precipitation, such as the radar reflectivity factor Z, rain rate R, and raindrop size distribution (DSD). A monitoring system that consisted of rain gauges and raindrop spectrometers was employed to monitor near-ground rainfall within a 5-km radius of the field site. The ground-based observations showed that acoustic waves could significantly affect the rainfall distribution and microstructure of precipitation particles. The average values of rainfall increased by 18.98%, 10.61%, and 8.74% within 2, 3, and 5 km, respectively, of the operation center with acoustic application. The changing trend of microphysical parameters of precipitation was roughly in line with variation of acoustic waves for stratiform cloud. Moreover, there was a good quadratic relationship between the spectral parameters λ and μ. Raindrop kinetic energy e K and the radar reflectivity factor Z both exhibited a power function relationship with R.

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Katja Friedrich, Jeffrey R. French, Sarah A. Tessendorf, Melinda Hatt, Courtney Weeks, Robert M. Rauber, Bart Geerts, Lulin Xue, Roy M. Rasmussen, Derek R. Blestrud, Melvin L. Kunkel, Nicholas Dawson, and Shaun Parkinson

Abstract

The spatial distribution and magnitude of snowfall resulting from cloud seeding with silver iodide (AgI) is closely linked to atmospheric conditions, seeding operations, and dynamical, thermodynamical, and microphysical processes. Here, microphysical processes leading to ice and snow production are analyzed in orographic clouds for three cloud-seeding events, each with light or no natural precipitation and well-defined, traceable seeding lines. Airborne and ground-based radar observations are linked to in situ cloud and precipitation measurements to determine the spatiotemporal evolution of ice initiation, particle growth, and snow fallout in seeded clouds. These processes and surface snow amounts are explored as particle plumes evolve from varying amounts of AgI released, and within changing environmental conditions, including changes in liquid water content (LWC) along and downwind of the seeding track, wind speed, and shear. More AgI did not necessarily produce more liquid equivalent snowfall (LESnow). The greatest amount of LESnow, largest area covered by snowfall, and highest peak snowfall produced through seeding occurred on the day with the largest and most widespread occurrence of supercooled drizzle, highest wind shear, and greater LWC along and downwind of the seeding track. The day with the least supercooled drizzle and the lowest LWC downwind of the seeding track produced the smallest amount of LESnow through seeding. The stronger the wind was, the farther away the snowfall occurred from the seeding track.

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G. Duan and T. Takemi

Abstract

The surface roughness aerodynamic parameters z 0 (roughness length) and d (zero-plane displacement height) are vital to the accuracy of the Monin–Obukhov similarity theory. Deriving improved urban canopy parameterization (UCP) schemes within the conventional framework remains mathematically challenging. The current study explores the potential of a machine-learning (ML) algorithm, a random forest (RF), as a complement to the traditional UCP schemes. Using large-eddy simulation and ensemble sampling, in combination with nonlinear least squares regression of the logarithmic-layer wind profiles, a dataset of approximately 4.5 × 103 samples is established for the aerodynamic parameters and the morphometric statistics, enabling the training of the ML model. While the prediction for d is not as good as the UCP after Kanda et al., the performance for z 0 is notable. The RF algorithm also categorizes z 0 and d with an exceptional performance score: the overall bell-shaped distributions are well predicted, and the ±0.5σ category (i.e., the 38% percentile) is competently captured (37.8% for z 0 and 36.5% for d). Among the morphometric features, the mean and maximum building heights (H ave and H max, respectively) are found to be of predominant influence on the prediction of z 0 and d. A perhaps counterintuitive result is the considerably less striking importance of the building-height variability. Possible reasons are discussed. The feature importance scores could be useful for identifying the contributing factors to the surface aerodynamic characteristics. The results may shed some light on the development of ML-based UCP for mesoscale modeling.

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Zexia Duan, C. S. B. Grimmond, Chloe Y. Gao, Ting Sun, Changwei Liu, Linlin Wang, Yubin Li, and Zhiqiu Gao

Abstract

Quantitative knowledge of the water and energy exchanges in agroecosystems is vital for irrigation management and modeling crop production. In this study, the seasonal and annual variabilities of evapotranspiration (ET) and energy exchanges were investigated under two different crop environments—flooded and aerobic soil conditions—using three years (June 2014–May 2017) of eddy covariance observations over a rice–wheat rotation in eastern China. Across the whole rice–wheat rotation, the average daily ET rates in the rice paddies and wheat fields were 3.6 and 2.4 mm day−1, respectively. The respective average seasonal ET rates were 473 and 387 mm for rice and wheat fields, indicating a higher water consumption for rice than for wheat. Averaging for the three cropping seasons, rice paddies had 52% more latent heat flux than wheat fields, whereas wheat had 73% more sensible heat flux than rice paddies. This resulted in a lower Bowen ratio in the rice paddies (0.14) than in the wheat fields (0.4). Because eddy covariance observations of turbulent heat fluxes are typically less than the available energy (R n − G; i.e., net radiation minus soil heat flux), energy balance closure (EBC) therefore does not occur. For rice, EBC was greatest at the vegetative growth stages (mean: 0.90) after considering the water heat storage, whereas wheat had its best EBC at the ripening stages (mean: 0.86).

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Jeremy E. Diem, Jonathan D. Salerno, Michael W. Palace, Karen Bailey, and Joel Hartter

Abstract

Substantial research on the teleconnections between rainfall and sea surface temperatures (SSTs) has been conducted across equatorial Africa as a whole, but currently no focused examination exists for western Uganda, a rainfall transition zone between eastern equatorial Africa (EEA) and central equatorial Africa (CEA). This study examines correlations between satellite-based rainfall totals in western Uganda and SSTs—and associated indices—across the tropics over 1983–2019. It is found that rainfall throughout western Uganda is teleconnected to SSTs in all tropical oceans but is connected much more strongly to SSTs in the Indian and Pacific Oceans than in the Atlantic Ocean. Increased Indian Ocean SSTs during boreal winter, spring, and autumn and a pattern similar to a positive Indian Ocean dipole during boreal summer are associated with increased rainfall in western Uganda. The most spatially complex teleconnections in western Uganda occur during September–December, with northwestern Uganda being similar to EEA during this period and southwestern Uganda being similar to CEA. During boreal autumn and winter, northwestern Uganda has increased rainfall associated with SST patterns resembling a positive Indian Ocean dipole or El Niño. Southwestern Uganda does not have those teleconnections; in fact, increased rainfall there tends to be more associated with La Niña–like SST patterns. Tropical Atlantic Ocean SSTs also appear to influence rainfall in southwestern Uganda in boreal winter as well as in boreal summer. Overall, western Uganda is a heterogeneous region with respect to rainfall–SST teleconnections; therefore, southwestern Uganda and northwestern Uganda require separate analyses and forecasts, especially during boreal autumn and winter.

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Tong Guo and Yanhong Tang

Abstract

Long-term variabilities in daily precipitation and temperature are critical for assessing the impacts of climate change on ecosystems. We characterized intra- and interannual variabilities in daily precipitation and temperature obtained from 1960 to 2015 at 78 meteorological stations on the Qinghai–Tibetan Plateau. The results show the following. 1) the intra-annual variability of daily precipitation increases for 55 meteorological stations with a rate of 0.08 mm decade−1. In contrast, the intra-annual variability markedly decreases for daily mean, daytime mean, and nighttime mean temperatures with a rate of 0.09°, 0.07°, and 0.12°C decade−1, respectively, at 90% or more of stations. 2) Variabilities of daily precipitation and temperatures are very sensitive to high altitudes (>3500 m). The intra- and interannual variabilities of daily precipitation significantly decrease at 1.0 and 0.07 mm (1000 m)−1, respectively. However, variations of high altitudes increase the intra- and interannual variabilities of daily mean temperature at 1.0° and 0.2°C (1000 m)−1, respectively. Moreover, the interannual variability of nighttime mean temperature varies at 0.3°C (1000 m)−1, the fastest rate among three temperature indices. 3) A larger mean annual precipitation is accompanied by a higher intra- and interannual variability of daily precipitation on the Qinghai–Tibetan Plateau; however, a higher mean annual temperature leads to lower variabilities of daily temperatures. This study illustrates that long-term climatic variability is understudied in alpine ecosystems characterized by high climatic sensitivity. Precipitation and temperature variabilities should be characterized to improve predictions of vulnerable ecosystems responding to climate change.

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Daeho Jin, Lazaros Oreopoulos, Dongmin Lee, Jackson Tan, and Nayeong Cho

Abstract

To better understand cloud–precipitation relationships, we extend the concept of cloud regimes developed from two-dimensional joint histograms of cloud optical thickness and cloud-top pressure from MODIS to include precipitation information. Taking advantage of the high-resolution IMERG precipitation dataset, we derive cloud–precipitation “hybrid” regimes by implementing a k-means clustering algorithm with advanced initialization and objective measures to determine the optimal number of clusters. By expressing the variability of precipitation rates within 1° grid cells as histograms and varying the relative weight of cloud and precipitation information in the clustering algorithm, we obtain several editions of hybrid cloud–precipitation regimes (CPRs) and examine their characteristics. In the deep tropics, when precipitation is weighted weakly, the cloud part centroids of the hybrid regimes resemble their counterparts of cloud-only regimes, but combined clustering tightens the cloud–precipitation relationship by decreasing each regime’s precipitation variability. As precipitation weight progressively increases, the shape of the cloud part centroids becomes blunter, while the precipitation part sharpens. When cloud and precipitation are weighted equally, the CPRs representing high clouds with intermediate to heavy precipitation exhibit distinct enough features in the precipitation parts of the centroids to allow us to project them onto the 30-min IMERG domain. Such a projection overcomes the temporal sparseness of MODIS cloud observations associated with substantial rainfall, suggesting great application potential for convection-focused studies for which characterization of the diurnal cycle is essential.

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Junshi Ito, Toshiyuki Nagoshi, and Hiroshi Niino
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