Comparison of Ventilated and Unventilated Air Temperature Measurements in Inland Dronning Maud Land on the East Antarctic Plateau

Surface temperature measurements with naturally ventilated (NV) sensors over the Antarctic Plateau are largely subject to systematic errors caused by solar radiative heating. Here we examined the radiative heating error in Dronning Maud Land on the East Antarctic Plateau using both the newly installed automatic weather stations (AWSs) at NDF and Relay Station and the existing AWSs at Relay Station and Dome Fuji. Two types of NV shields were used in these AWSs: a multiplate radiation shield and a simple cylinder-shaped shield. In austral summer, the temperature bias between the force-ventilated (FV) sensor and the NV sensor never reached zero because of continuous sunlight. The hourly mean temperature errors reached up to 8°C at noon on a sunny day with weak wind conditions. The errors increased linearly with increasing reflected shortwave radiation and decreased nonlinearly with increasing wind speed. These features were observed in both the multiplate and the cylinder-shaped shields. The magnitude of the errors of the multiplate shield was much larger than that of the cylinder-shaped shield. To quantify the radiative errors, we applied an existing correction model based on the regression approach and successfully reduced the errors by more than 70% after the correction. This indicates that we can use the corrected temperature data instead of quality controlled data, which removed warm bias during weak winds in inland Dronning Maud Land.

How Does El Niño Affect Predictability Barrier of Its Accompanied Positive Indian Ocean Dipole Event?

The effects of El Niño on the predictability of positive Indian Ocean dipole (pIOD) events are investigated by using the GFDL CM2p1 coupled model from the perspective of error growth. The results show that, under the influence of El Niño, the summer predictability barrier (SPB) for pIOD tends to intensify and the winter predictability barrier (WPB) is weakened. Since the reason for the weakening of WPB has been explained in a previous study, the present study attempts to explore why the SPB is enhanced. The results demonstrate that the initial sea temperature errors, which are most likely to induce SPB for pIOD with El Niño, possess patterns similar to those for pIOD without El Niño, whose dominant errors concentrate in the tropical Pacific Ocean (PO), with a pattern of negative SST errors occurring in the eastern and central PO and subsurface sea temperature errors being negative in the eastern PO and positive in the western PO. By tracking the development of such initial errors, it is found that the initial errors over PO lead to anomalous westerlies in the southeastern Indian Ocean (IO) through the effect of double-cell Walker circulation. Such westerly anomalies are inhibited by the strongest climatological easterly wind and the southeasterlies related to the pIOD event itself in summer, while they are enhanced by El Niño. This competing effect causes the intensified seasonal variation in latent heat flux, with much less loss in summer under the effect of El Niño. The greater suppression of the loss of latent heat flux favors the positive sea surface temperature (SST) errors developing much faster in the eastern Indian Ocean in summer, and eventually induces an enhanced SPB for pIOD due to El Niño.

Forecasting Algorithm Based on Temperature Error Prediction Using Kalman Filter for Management System Development

The study offers a new method of collection and processing of meteorological data from the meteorological service based on observations and correction of numerical weather forecast errors using a new prediction algorithm. This algorithm vastly increases the accuracy of the short-term forecast of outdoor air temperature, which is subject to uncertainty due to the stochastic nature of atmospheric processes. Processing of temperature data using Kalman filter provides the decrease in predicted temperature errors. The main setup methods of Kalman filter have been examined. The article also describes the implementation of accuracy improving algorithm of predicted temperature using Python.

COMPARISON OF METHODS FOR CONTROLLED THERMAL DEFORMATIONS IN MACHINE TOOLS

Progress in improving the accuracy of metal-cutting machines is inextricably linked and driven by deeper knowledge gained through the study of thermal processes and effects occurring in machines, which can be used to manage them. This led to the dominance of temperature errors in the balance of machine accuracy, the share of which changed from 20-30% to 70% during the period from 1950 to 2020, which is determined by the absolute value of the achievable machine accuracy. Types and forms of compensation methods were formed (1990-2020), which were based on the use of linear and nonlinear regression or correlation methods. Performing experiments can establish the functional relationship between the measured temperature in the machine nodes and the amount of displacement. With good repeatability and stable reproducibility of the result, an equation expresses this functional relationship. Applying this equation to a program, a control device compensates the thermal deformations. However, in all cases, it is necessary to determine the number and location of temperature measurements on the machine, determining the compensation accuracy. The proposed sensorless model is based on a thermal behavior model and does not require temperature measurements. A method is presented and justified for estimating the number of temperature measurement locations based on thermophysical analysis by applying the finite element method in comparison with the analytical method in order to achieve the required compensation accuracy. For several machine tool types, a comparison is given regarding the control method of the TCP spindle displacement without sensors and with temperature sensors. The limits of their rational use are presented.

Characterising extratropical near tropopause analysis humidity biases and their radiative effects on temperature forecasts

<p>A cold bias in the extratropical lowermost stratosphere in forecasts is one of the most prominent systematic temperature errors in numerical weather prediction models. Hypothesized causes of this bias include radiative effects from a collocated moist bias in model analyses. Such biases would be expected to affect extratropical dynamics and result in the misrepresentation of wave propagation at tropopause level. Here the extent to which these biases are connected is quantified. Observations from radiosondes are compared to operational analyses and forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) and Met Office Unified Model (MetUM) to determine the magnitude and vertical structure of these biases. Both operational models over-estimate lowermost stratospheric specific humidity by around 70% of the observed values on average, around 1km above the tropopause. This moist bias is already present in the initial conditions and changes little in forecasts over the first five days. Though temperatures are represented well in the analyses, the IFS forecasts anomalously cool in the lower stratosphere, relative to verifying radiosonde observations, by 0.2K per day. The IFS single column model is used to show this temperature change can be attributed to increased long-wave radiative cooling due to the lowermost stratospheric moist bias in the initial conditions. However, the MetUM temperature biases cannot be entirely attributed to the moist bias, and another significant factor must be present. These results highlight the importance of improving the humidity analysis to reduce the extratropical lowermost stratospheric cold bias in forecast models and the need to understand and mitigate the causes of the moist bias in these models.</p>

Evaluation of near-surface temperature forecasts against super-site observations

<p><span><strong>ECMWF’s medium-range forecasts of near-surface weather parameters, such as 2 m temperature, humidity and 10 m wind speed, have become more skilful over the years, following the trend of improvements in the forecast skill of upper-air fields. However, they are still affected by systematic errors which have proved difficult to eliminate. Systematic forecast errors in temperature and humidity near the surface can be better understood by also examining errors higher up in the atmospheric boundary layer and in the soil. Meteorological observatories, also known as super-sites, provide long-term observational records of such vertical profiles. ECMWF started to use data from super-sites more systematically to evaluate the quality of forecasts in the lowest part of the atmosphere (up to 100m) and in the soil, in an attempt to disentangle sources of forecast error in near-surface weather parameters. Findings for 2-metre temperature errors in ECMWF forecasts at European super-sites suggest that the errors are partly the result of the model exchanging too much energy between the atmosphere and the land. However, the influence of other factors, such as errors resulting from the representation of vegetation in semi-arid areas and from small-scale variations in vegetation and soil type near measurement stations, mean that it is difficult to adjust the energy exchange in a way which leads to an overall error reduction on the European scale. </strong></span></p>