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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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Basivi Radhakrishna and Thota Narayana Rao

Abstract

The diurnal cycle of rainfall by large-scale systems (LSS) and small-scale systems (SSS) has been studied over a complex terrain region (Gadanki) in southern peninsular India using eight years of data from a network of 36 rain gauges. The diurnal cycle of accumulated rainfall by LSS and SSS shows peaks at 2200 and 1900 LT, respectively, during the southwest monsoon (SWM) and at 1900 and ~1700 LT during the northeast monsoon (NEM). Irrespective of the season and system size, the diurnal mode is the dominant mode of variation; it explains ~60% of variance during the SWM and ~54% during the NEM in LSS presence and explains ~43% of variance during the SWM and ~36% during the NEM in SSS presence. The correlation structure of rainfall is anisotropic with an axis ratio of ~1.5 for LSS and ~1.4 for SSS. Propagating systems are prevalent (80%–90% of times produce rain) in the presence of LSS during both seasons and play a dominant role in altering the diurnal cycle of rainfall over the Gadanki region. The conducive environment, like the presence of large relative humidity, updrafts in the lower and midtroposphere, and large lower and small midtropospheric shears, favors convective initiation and propagation of precipitating systems during LSS in SWM and NEM. The atmosphere favors convective initiation between 1800 and 2000 LT. The dry midtroposphere and weak upward motion in the midtroposphere inhibit mesoscale organization and form SSS during the SWM. During the NEM, a somewhat drier midtroposphere than in LSS and small wind shear in the lower troposphere (“L-shear”) inhibit the convective organization and form SSS between 1500 and 1800 LT.

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Jinxin Wang and Xiao-Ming Hu

Abstract

This study evaluated the Weather Research and Forecasting (WRF) Model sensitivity to different planetary boundary layer (PBL) schemes (the YSU and MYJ schemes) and urban schemes including the bulk scheme (BULK), single-layer urban canopy model (UCM), multilayer building environment parameterization (BEP) model, and multilayer building energy model (BEM). Daily reinitialization simulations were conducted over Dallas–Fort Worth during a dry summer month (July 2011) and a wet summer month (July 2015) with weaker (stronger) daytime (nocturnal) UHI in 2011 than 2015. All urban schemes overestimated the urban daytime 2-m temperature in both summers, but BEP and BEM still reproduced the daytime urban cool island in the dry summer. All urban schemes reproduced the nocturnal urban heat island, with BEP producing the weakest one due to its unrealistic urban cooling. BULK and UCM overestimated the urban canopy wind speed, while BEP and BEM underestimated it. The urban schemes showed prominent impact on daytime PBL profiles. UCM + MYJ showed a superior performance than other configurations. The relatively large (small) aspect ratio between building height and road width in UCM (BEM) was responsible for the overprediction (underprediction) of urban canopy temperature. The relatively low (high) building height in UCM (BEM) was responsible for the overprediction (underprediction) of urban canopy wind speed. Improving urban schemes and providing realistic urban parameters were critical for improving urban canopy simulation.

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Brittany N. Carson-Marquis, Jianglong Zhang, Peng Xian, Jeffrey S. Reid, and Jared W. Marquis

Abstract

When unaccounted for in numerical weather prediction (NWP) models, heavy aerosol events can cause significant unrealized biases in forecast meteorological parameters such as surface temperature. To improve near-surface forecasting accuracies during heavy aerosol loadings, we demonstrate the feasibility of incorporating aerosol fields from a global chemical transport model as initial and boundary conditions into a higher-resolution NWP model with aerosol–meteorological coupling. This concept is tested for a major biomass burning smoke event over the northern Great Plains region of the United States that occurred during summer of 2015. Aerosol analyses from the global Navy Aerosol Analysis and Prediction System (NAAPS) are used as initial and boundary conditions for Weather Research and Forecasting Model with Chemistry (WRF-Chem) simulations. Through incorporating more realistic aerosol direct effects into the WRF-Chem simulations, errors in WRF-Chem simulated surface downward shortwave radiative fluxes and near-surface temperature are reduced when compared with surface-based observations. This study confirms the ability to decrease biases induced by the aerosol direct effect for regional NWP forecasts during high-impact aerosol episodes through the incorporation of analyses and forecasts from a global aerosol transport model.

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