SwitchBot Weather Station

B. Malozyomov, N. Martyushev, E. Voitovich, Roman V. Kononenko, V. Konyukhov, Vadim Tynchenko, V. Kukartsev, Y. Tynchenko

Autonomous power systems serving remote areas with weather stations with small settlements are characterized by a fairly high cost of generating electricity and the purchase and delivery of fuel. In addition, diesel power plants require regular maintenance, have a relatively short service life during continuous operation and produce a large amount of emissions into the environment. This article discusses various methods of placing solar panels in the space for the autonomous power supply of weather station equipment. The principles of these methods are described and their advantages and disadvantages are outlined. The optimal algorithms of functioning for photomodules are described and their comparison regarding the main, significant parameters is carried out. The choice of the most effective algorithm for use at a weather station is made. The effective positioning of solar panels is also calculated, and positioning conditions are determined depending on the territorial location and various environmental conditions. Simulation of the power supply system of a weather station consisting of solar panels, batteries and inverters is performed. As a result, a practical example of the application of the method of selecting the optimal composition of equipment for a hybrid power system of a weather station territorially located in Siberia with different configurations of equipment is considered. In numerical terms, it was possible to reduce the cost of power equipment operation by more than 60% with a fairly low payback period of 5.5 years and an increased reliability of the power system, which is very important for autonomous power systems of northern weather stations.

Peiyuan Li, Yin Yu, Daning Huang, Zhi-hua Wang, Ashish Sharma

Heatwaves lead to catastrophic consequences on public health and the economy. Accurate and timely predictions of regional heatwaves can improve climate preparedness and foster decision‐making to alleviate the burdens due to climate change. In this paper, we propose a heatwave prediction algorithm based on a novel deep learning model, that is, Graph Neural Network (GNN). This new GNN framework can provide real time warnings of the sudden occurrence of regional heatwaves with high accuracy at lower costs of computation and data collection. In addition, its interpretable structure unravels the spatiotemporal patterns of regional heatwaves and helps to enrich our understanding of the general climate dynamics and the causal influences between locations. The proposed GNN framework can be applied for the detection and prediction of other extreme or compound climate events, which calls for future studies.

P. Manshausen, Y. Cohen, Jaideep Pathak, Michael S. Pritchard, P. Garg, Morteza Mardani, K. Kashinath, S. Byrne, Noah D. Brenowitz

Data assimilation of observations into full atmospheric states is essential for weather forecast model initialization. Recently, methods for deep generative data assimilation have been proposed which allow for using new input data without retraining the model. They could also dramatically accelerate the costly data assimilation process used in operational regional weather models. Here, in a central US testbed, we demonstrate the viability of Score‐based Data Assimilation (SDA) in the context of realistically complex km‐scale weather. We train an unconditional diffusion model to generate snapshots of a state‐of‐the‐art km‐scale analysis product, the High Resolution Rapid Refresh. Then, using SDA to incorporate sparse weather station data, the model produces maps of precipitation and surface winds. The generated fields display physically plausible structures, such as gust fronts, and sensitivity tests confirm learned physics through multivariate relationships. Preliminary skill analysis shows the approach already outperforms a naive baseline of the High‐Resolution Rapid Refresh system itself. By incorporating observations from 40 weather stations, 10% lower RMSEs on left‐out stations are attained. Despite some lingering imperfections such as insufficiently disperse ensemble DA estimates, we find the results overall an encouraging proof of concept, and the first at km‐scale. It is a ripe time to explore extensions that combine increasingly ambitious regional state generators with an increasing set of in situ, ground‐based, and satellite remote sensing data streams.

Yetang Wang, Xueying Zhang, Wentao Ning, M. Lazzara, M. Ding, C. Reijmer, P. Smeets, P. Grigioni, P. Heil, E. Thomas, David E Mikolajczyk, L. J. Welhouse, L. Keller, Zhao-Hui Zhai, Yuqi Sun, S. Hou

Abstract. A new meteorological dataset derived from records of Antarctic automatic weather stations (here called the AntAWS dataset) at 3 h, daily and monthly resolutions including quality control information is presented here. This dataset integrates the measurements of air temperature, air pressure, relative humidity, and wind speed and direction from 267 Antarctic AWSs obtained from 1980 to 2021. The AWS spatial distribution remains heterogeneous, with the majority of instruments located in near-coastal areas and only a few inland on the East Antarctic Plateau. Among these 267 AWSs, 63 have been operating for more than 20 years and 27 of them in excess of more than 30 years. Of the five meteorological parameters, the measurements of air temperature have the best continuity and the highest data integrity. The overarching aim of this comprehensive compilation of AWS observations is to make these data easily and widely accessible for efficient use in local, regional and continental studies; it may be accessed at https://doi.org/10.48567/key7-ch19 (Wang et al., 2022). This dataset is invaluable for improved characterization of the surface climatology across the Antarctic continent, to improve our understanding of Antarctic surface snow–atmosphere interactions including precipitation events associated with atmospheric rivers and to evaluate regional climate models or meteorological reanalysis products.

Tao Han, Song Guo, Zhenghao Chen, Wanghan Xu, Lei Bai