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We present the astrometric calibration of the Beijing–Arizona Sky Survey (BASS). The BASS astrometry was tied to the International Celestial Reference Frame via the Gaia Data Release 2 reference catalog. For effects that were stable throughout the BASS observations, including differential chromatic refraction and the low charge transfer efficiency of the CCD, we corrected for these effects at the raw image coordinates. Fourth-order polynomial intermediate longitudinal and latitudinal corrections were used to remove optical distortions. The comparison with the Gaia catalog shows that the systematic errors, depending on color or magnitude, are less than 2 milliarcseconds (mas). The position systematic error is estimated to be about −0.01 ± 0.7 mas in the region between 30° and 60° of decl. and up to −0.07 ± 0.9 mas in the region north of decl. 60°.

 

Developing urban land surface models for modeling cities at high resolutions needs to better account for the city‐specific multi‐scale land surface heterogeneities at a reasonable computational cost. We propose using an encoder‐decoder convolutional neural network to develop a computationally efficient model for predicting the mean velocity field directly from urban geometries. The network is trained using the geometry‐resolving large eddy simulation results. Systematic testing on urban structures with increasing deviations from the training geometries shows the prediction error plateaus at 15%, compared to errors sharply increasing up to 35% in the null models. This is explained by the trained model successfully capturing the effects of pressure drag, especially for tall buildings. The prediction error of the aerodynamic drag coefficient is reduced by 32% compared with the default parameterization implemented in mesoscale modeling. This study highlights the potential of combining computational fluid dynamics modeling and machine learning to develop city‐specific parameterizations.

 

Autonomous aerial manipulators have great potentials to assist humans or even fully automate manual labor-intensive tasks such as aerial cleaning, aerial transportation, infrastructure repair, and agricultural inspection and sampling. Reinforcement learning holds the promise of enabling persistent autonomy of aerial manipulators because it can adapt to different situations by automatically learning optimal policies from the interactions between the aerial manipulator and environments. However, the learning process itself could experience failures that can practically endanger the safety of aerial manipulators and hence hinder persistent autonomy. In order to solve this problem, we propose for the aerial manipulator a self-reflective learning strategy that can smartly and safely finding optimal policies for different new situations. This self-reflective manner consists of three steps: identifying the appearance of new situations, re-seeking the optimal policy with reinforcement learning, and evaluating the termination of self-reflection. Numerical simulations demonstrate, compared with conventional learning-based autonomy, our strategy can significantly reduce failures while still can finish the given task.

 

Abstract. Subseasonal-to-seasonal (S2S) prediction, especially the prediction of extreme hydroclimate events such as droughts and floods, is not only scientifically challenging, but also has substantial societal impacts. Motivated by preliminary studies, the Global Energy and Water Exchanges(GEWEX)/Global Atmospheric System Study (GASS) has launched a new initiativecalled “Impact of Initialized Land Surface Temperature and Snowpack on Subseasonal to Seasonal Prediction” (LS4P) as the first international grass-roots effort to introduce spring land surface temperature(LST)/subsurface temperature (SUBT) anomalies over high mountain areas as acrucial factor that can lead to significant improvement in precipitationprediction through the remote effects of land–atmosphere interactions. LS4P focuses on process understanding and predictability, and hence it is differentfrom, and complements, other international projects that focus on theoperational S2S prediction. More than 40 groups worldwide have participated in this effort, including 21 Earth system models, 9 regionalclimate models, and 7 data groups. This paper provides an overview of the history and objectives of LS4P, provides the first-phase experimental protocol (LS4P-I) which focuses on the remote effect ofthe Tibetan Plateau, discusses the LST/SUBT initialization, and presents thepreliminary results. Multi-model ensemble experiments and analyses ofobservational data have revealed that the hydroclimatic effect of the springLST on the Tibetan Plateau is not limited to the Yangtze River basin but may have a significant large-scale impact on summer precipitation beyond EastAsia and its S2S prediction. Preliminary studies and analysis have alsoshown that LS4P models are unable to preserve the initialized LST anomaliesin producing the observed anomalies largely for two main reasons: (i) inadequacies in the land models arising from total soil depths which are tooshallow and the use of simplified parameterizations, which both tend to limit the soil memory; (ii) reanalysis data, which are used for initial conditions, have large discrepancies from the observed mean state andanomalies of LST over the Tibetan Plateau. Innovative approaches have beendeveloped to largely overcome these problems. 

Ionospheric day‐to‐day variability is essential for understanding the space environment, while it is still challenging to properly quantify and forecast. In the present work, the day‐to‐day variability of F2 layer peak electron densities (NmF2) is examined from both observational and modeling perspectives. Ionosonde data over Wuhan station (30.5°N, 114.5°E; 19.3°N magnetic latitude) are compared with simulations from the specific dynamics Whole Atmosphere Community Climate Model with thermosphere and ionosphere eXtension (SD‐WACCM‐X) and the Thermosphere‐Ionosphere‐Electrodynamics General Circulation Model (TIEGCM) in 2009 and 2012. Both SD‐WACCM‐X and TIEGCM are driven by the realistic 3 h geomagnetic index and daily solar input, and the former includes self‐consistently solved physics and chemistry in the lower atmosphere. The correlation coefficient between observations and SD‐WACCM‐X simulations is much larger than that of the TIEGCM simulations, especially during dusk in 2009 and nighttime in 2012. Both the observed and SD‐WACCM‐X simulated day‐to‐day variability of NmF2 reveal a similar day‐night dependence in 2012 that increases large during the nighttime and decreases during the daytime, and shows favorable consistency of daytime variability in 2009. Both the observations and SD‐WACCM‐X simulations also display semiannual variations in nighttime NmF2 variability, although the month with maximum variability is slightly different. However, TIEGCM does not reproduce the day‐night dependence or the semiannual variations well. The results emphasize the necessity for realistic lower atmospheric perturbations to characterize ionospheric day‐to‐day variability. This work also provides a validation of the SD‐WACCM‐X in terms of ionospheric day‐to‐day variability.

 

Ionic liquids with bifluoride anions possess properties such as high conductivity, wide electrochemical windows, and low viscosity that make them attractive materials for various electrochemical devices. However, owing to the lack of reliable synthetic routes, bifluoride‐based ionic liquids have seldom been explored. Herein, an autocatalytic strategy for the HF‐free synthesis of bifluoride‐based sulfonamide ionic liquids based on the sulfur(VI) fluoride exchange (SuFEx) reaction is reported. This reaction requires no chromatographic purification, and yields are quantitative. The thermophysical properties (phase transition behavior and decomposition temperature) and electrochemical stabilities of the resulting products were studied. The products with alkyl, aryl, and perfluoroalkyl side chains exhibited extraordinarily wide electrochemical windows (up to 6.0 V) with reproducible results among multiple replicate measurements.