Frictional heating in a thick fault zone with empirical slip-weakening friction: implications for slip parameter estimation from temperature observations in deep fault drilling

The strain energy released during an earthquake is consumed by processes related to seismic radiation or dissipation. Deep fault drilling and subsequent temperature measurements in a thick fault zone immediately after an event have provided important insights into this dissipation process. By employing an analytical solution to the heat conduction problem, which involves the sudden injection of an infinitesimally thin heat source into an infinite medium, previous drilling projects have estimated the strength of the heat source and the level of shear stress from observed temperature anomalies. However, it is unclear under what conditions this analytical source solution can be regarded as a good approximation for the thick fault problem, a situation which has led to uncertainty of the approximation error in these previous studies. In this study, I first derived an analytical solution for the thick fault problem that accounted for experimentally derived slip-weakening friction. I then validated the derived solution both analytically and numerically. Using the derived thick solution, I next demonstrated that the thick, planar, and source solutions can be considered equivalent under the typical conditions of the previous drilling projects. Therefore, the slip parameters estimated by using the source solution obtained by these studies are appropriate. These results suggest that coseismic information with spatio-temporal extent, such as shear stress and friction coefficient, are lost due to heat diffusion when the temperature observations are conducted; thus, they cannot be inferred directly from observed temperature anomalies. These results also suggest that for most drilling projects, including future ones, the observed temperature distribution can be well explained by using the source solution instead of the thick solution as long as coseismic slip is not markedly delocalized and the spatial extent of the temperature measurements is not significantly larger than the diffusion length.

BiLSTM-I: A Deep Learning-Based Long Interval Gap-Filling Method for Meteorological Observation Data

Complete and high-resolution temperature observation data are important input parameters for agrometeorological disaster monitoring and ecosystem modelling. Due to the limitation of field meteorological observation conditions, observation data are commonly missing, and an appropriate data imputation method is necessary in meteorological data applications. In this paper, we focus on filling long gaps in meteorological observation data at field sites. A deep learning-based model, BiLSTM-I, is proposed to impute missing half-hourly temperature observations with high accuracy by considering temperature observations obtained manually at a low frequency. An encoder-decoder structure is adopted by BiLSTM-I, which is conducive to fully learning the potential distribution pattern of data. In addition, the BiLSTM-I model error function incorporates the difference between the final estimates and true observations. Therefore, the error function evaluates the imputation results more directly, and the model convergence error and the imputation accuracy are directly related, thus ensuring that the imputation error can be minimized at the time the model converges. The experimental analysis results show that the BiLSTM-I model designed in this paper is superior to other methods. For a test set with a time interval gap of 30 days, or a time interval gap of 60 days, the root mean square errors (RMSEs) remain stable, indicating the model’s excellent generalization ability for different missing value gaps. Although the model is only applied to temperature data imputation in this study, it also has the potential to be applied to other meteorological dataset-filling scenarios.

Historic temperature observations on Nordaustlandet, north-east Svalbard

Long-term meteorological data for the Arctic are sparse. One of the longest quasi-continuous temperature time series in the High Arctic is the extended Svalbard Airport series, providing daily temperature data from 1898 until the present. Here, I derive an adjustment to historic temperature observations on the island of Nordaustlandet, north-east Svalbard, in order to link these to the extended Svalbard Airport series. This includes the Haudegen observations at Rijpfjorden during 1944/45 and a previously unrecognized data set obtained by the Norwegian hunters and trappers Gunnar Knoph and Henry Rudi during their wintering at Rijpfjorden in 1934/35. The adjustment is based on data from an automatic weather station at Rijpfjorden during 2014–16 and verified with other independent historic temperature observations on Nordaustlandet. An analysis of the Haudegen radiosonde data indicates that the surface temperature observations at Rijpfjorden are generally well correlated with the free tropospheric temperatures at 850 hPa, but occasionally show the occurrence of boundary-layer inversions during winter, where local temperatures fall substantially below what is expected from the regression. The adjusted historic observations from Nordaustlandet can, therefore, be used to fill remaining gaps in the extended Svalbard Airport series.

Experiences in using low-cost sensors to observe road weather

<p>Road weather information is essential for keeping the roads well maintained and safe during wintertime. Main source of road weather observations are road weather stations, but IoT (Internet of things) sensor technology provides new ways to observe road weather. Finnish Meteorological Institute (FMI) and Fintraffic Road are studying whether such IoT technology could help increase spatial density and/or improve coverage in the observation network and whether these additional observations could also be used to improve road weather forecasts. Around 100 autonomous battery-operated low-cost IoT sensors based on LoRaWAN communication technology were installed into the roadside area of a motorway in southern Finland and at the Sodankylä airport test track during winter 2020. Most of the sensors were of the types UC11-T1 from Ursalink and ELT-2 from ELSYS AB, but there were a few MCF-LW12TERWP sensors from MCF88 as well. All sensors measure air temperature and humidity and the MCF sensors also measure air pressure. Some of the sensors were installed at a weather station and some at road weather stations to enable data comparison with reference stations. During wintertime the IoT sensors’ air temperature measurements correspond rather well to the reference measurements. However, during other times of the year the solar radiation often causes warm bias to the measurements. The bias is reduced when the sensors are installed inside radiation shields. However, the reliability of the IoT devices needs improvement, as several sensors stopped working during the measurement campaign. This was probably caused by a firmware bug, that led to excess power consumption and emptying of batteries in some of the devices.</p><p>The FMI road weather model uses surface temperature observations in the model initialization to improve the forecasts. As the model surface temperature is forced to the observed surface temperature, the air temperature measurements don’t have that much effect in the initialization. When there are no surface temperature observations available at the forecast location, the model uses values interpolated from road weather station observations. The interpolation is done with the universal kriging method, where elevation is used as an explanatory variable. In this project we studied whether air temperature observations from IoT sensors could be used as explanatory variable as well. The results thus far show that use of air temperature observations from road weather stations improves the interpolated surface temperature values at least in some situations. However, this is rather location dependent. Initial results suggest that IoT observations would be useful this way as well. According to the results, IoT observations show potential to improve road weather monitoring and forecasting, but more studies are still needed.</p>

International Quality-Controlled Ocean Database (IQuOD) v0.1: The Temperature Uncertainty Specification

Ocean temperature observations are crucial for a host of climate research and forecasting activities, such as climate monitoring, ocean reanalysis and state estimation, seasonal-to-decadal forecasts, and ocean forecasting. For all of these applications, it is crucial to understand the uncertainty attached to each of the observations, accounting for changes in instrument technology and observing practices over time. Here, we describe the rationale behind the uncertainty specification provided for all in situ ocean temperature observations in the International Quality-controlled Ocean Database (IQuOD) v0.1, a value-added data product served alongside the World Ocean Database (WOD). We collected information from manufacturer specifications and other publications, providing the end user with uncertainty estimates based mainly on instrument type, along with extant auxiliary information such as calibration and collection method. The provision of a consistent set of observation uncertainties will provide a more complete understanding of historical ocean observations used to examine the changing environment. Moving forward, IQuOD will continue to work with the ocean observation, data assimilation and ocean climate communities to further refine uncertainty quantification. We encourage submissions of metadata and information about historical practices to the IQuOD project and WOD.