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Jing Chen, Ji Wang, Runsheng Lin, and Li Lu

Abstract

The outdoor events of the 2022 Winter Olympics and Paralympics will be held in the mountain areas of Beijing–Zhangjiakou, North China, where there is a complete reliance on artificial snow production owing to the dry and cold weather conditions. To assess how favorable the meteorological conditions are to snowmaking at the mountain venues, we reconstructed the daily wet-bulb temperature by adopting the thin-plate smoothing spline function method, and then assessed the potential number of snowmaking days at eight weather stations (928–2098 m a.s.l.) from October to the next April (i.e., the ski season) during the period 1978–2017. Results showed that artificial snow production would have been possible on 121(±14) to 171(±12) days on average at the stations with the increases of altitude, and the number of days decreased at rates of 4.3–5.1 days per decade across four decades of the study period. The cause of the decrease was the warming trend, which affected the number of days in low-altitude sites simultaneously, but the reduction was delayed with increased elevation. At monthly scale, the number of snowmaking days was robust in wintertime but reduced in other months of the ski season, particularly in March in more recent sub-periods at high-altitude stations, which was determined by the increase in high values of daily mean wet-bulb temperature. Further improvements in assessing snowmaking conditions require detailed microclimatic studies to reduce the uncertainties caused by meteorological conditions, as well as combination with model-based methods to determine potential future changes.

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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.

Open access
Christopher P. Loughner, Benjamin Fasoli, Ariel F. Stein, and John C. Lin

Abstract

The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) is a state-of-the-science atmospheric dispersion model that is developed and maintained at the National Oceanic Atmospheric Administration’s Air Resources Laboratory. In the early 2000s, HYSPLIT served as the starting point for development of the Stochastic Time-Inverted Lagrangian Transport (STILT) model that emphasizes backward-in-time dispersion simulations to determine source regions of receptors. STILT continued its separate development and gained a wide user base. Since STILT was built on a now outdated version of HYSPLIT and lacks long-term institutional support to maintain the model, incorporating STILT features into HYSPLIT allows these features to stay up to date. This paper describes the STILT features incorporated into HYSPLIT, which include a new vertical interpolation algorithm for WRF-derived meteorological input files, a detailed algorithm for estimating boundary layer height, a new turbulence parameterization, a vertical Lagrangian time scale that varies in time and space, a complex dispersion algorithm, and two new convection schemes. An evaluation of these new features was performed using tracer release data from the Cross Appalachian Tracer Experiment and the Across North America Tracer Experiment. Results show that the dispersion module from STILT, which takes up to double the amount of time to run, is less dispersive in the vertical direction and is in better agreement with observations when compared with the existing HYSPLIT option. The other new modeling features from STILT were not consistently statistically different than existing HYSPLIT options. Forward-time simulations from the new model were also compared with backward-in-time equivalents and were found to be statistically comparable to one another.

Open access
Lijuan Wang, Hongchao Zuo, and Wei Wang

Abstract

Fengyun-4A (FY-4A) is a geostationary meteorological satellite with four advanced payloads, which can be used to quantitatively detect Earth’s atmospheric system with multispectral and high spatial and temporal resolution. However, the applicable model limits the application of the FY-4A satellite data. In this paper, the empirical statistical model developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor is extended for FY-4A Advanced Geosynchronous Radiation Imager (AGRI), and it is applied to observed data to evaluate the applicability of the model for AGRI measurements. To improve the accuracy of radiation estimation, the artificially intelligent particle swarm optimization (PSO) algorithm was used for model optimizing. Results show that the estimated radiation has diurnal variation that is in accord with the characteristics of radiation variation. The estimated net surface shortwave radiation (Sn) and observed values show good correlation. However, large deviations from observations are found in the estimated values when the empirical model based on MODIS is directly used to process AGRI data. Thus, the empirical statistical model based on MODIS can be applied to AGRI data, but the empirical parameters need to be revised. Optimization of the empirical statistical model by the PSO algorithm can effectively improve the accuracy of the radiation estimate. The mean absolute percentage error (MAPE) of Sn estimated by optimized models is reduced to 15%. The MAPE of the net surface longwave radiation (Ln) estimated by optimized models is reduced to 31%, and the MAPE of the net radiation (Rn) estimated by optimized models is reduced to 27%. However, for the uncertainty caused by error accumulation effect, the influence of PSO optimization on Rn is not as obvious as that of Ln. However, the analysis of error distribution shows that PSO optimization does improve the estimation results of Rn. Based on AGRI data, the surface radiation can be estimated simply, and the regional or larger-scale surface radiation retrieval can quickly be realized by this method, which has large application potential and popularization value.

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Yi-Jie Zhu, Jennifer M. Collins, and Philip J. Klotzbach

Abstract

Understanding tropical cyclone wind speed decay during the postlandfall stage is critical for inland hazard preparation. This paper examines the spatial variation of wind speed decay of tropical cyclones over the continental United States. We find that tropical cyclones making landfall over the Gulf Coast decay faster within the first 24 h after landfall than those making landfall over the Atlantic East Coast. The variation of the decay rate over the Gulf Coast remains larger than that over the Atlantic East Coast for tropical cyclones that had made landfall more than 24 h prior. Besides an average weaker tropical cyclone landfall intensity, the near-parallel trajectory and the proximity of storms to the coastline also help to explain the slower postlandfall wind speed decay for Atlantic East Coast landfalling tropical cyclones. Tropical cyclones crossing the Florida Peninsula only slowly weaken after landfall, with an average of less than 20% postlandfall wind speed drop while transiting the state. The existence of these spatial variations also brings into question the utility of a uniform wind decay model. While weak intensity decay over the Florida Peninsula is well estimated by the uniform wind decay model, the error from the uniform wind decay model increases with tropical cyclones making direct landfall more parallel to the Atlantic East Coast. The underestimation of inland wind speed by the uniform wind decay model found over the western Gulf Coast brings attention to the role of land–air interactions in the decay of inland tropical cyclones.

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