Processing algorithm of weekly records of the Roztochia landscape-geophysical station thermograph М-16АН as a response source of air temperature data

Abstract

Formulation of the problem. During the database processing of the Roztochia landscape-geophysical station (RLGS), located in the village of Bryukhovychi, Lviv, an air temperature data gap for 1990–1991 was found. The task of the research was to find those sources about air temperature at RLGS, which would allow us to fill in the gaps during the night hours when, unfortunately, observers did not make measurements. Problems of further research. In comparison with the method adopted in Ukraine for processing weekly thermograph tapes, in this study, it is proposed to correct the air temperature value during their processing, compensating for accelerated or slowed rotation of the weekly thermograph drum. It is suggested to use only those dry bulb measurements carried out on days with cloudy or rainy weather. The purpose. The main goal was to find an algorithm for processing weekly thermograph tapes under the conditions of a partial absence of temperature measurements using a dry thermometer by an observer to fill in the gaps regarding night air temperature data. Research methods. The air temperature values falling during the measurement period were read from the thermograph tapes, and an electronic table was formed. The temperature values (difference estimation) were compared with the corresponding ones recorded in the "Books" of KM-1. At the same time, it was necessary to make two new corrections. The first correction will be made along the ordinate axis, changing the value of the temperature recorded by the thermograph compared to the values of the temperature measured by the dry thermometer. The second correction was made along the abscissa axis, compensating for the drum's slowed down or accelerated rotation. Presentation of the main research material. A brief description of the proposed algorithm for thermograph tape processing is as follows. In the spreadsheet, in separate columns, we record the temperature values during the observation periods: a) by dry thermometer and b) by thermograph at the points corresponding to the observation periods. Subtracting columns (a) and (b) values, we determine those dry bulb temperature values suitable for calculating corrections. We reject too significant differences that occur during rapid temperature changes. Next, we look for points on the thermograph tape that serve as time benchmarks (the starting and ending points of the temperature curve and the places where the observer draws vertical lines). These temperature values will form column (c). It will additionally include the temperature values obtained by reading the temperature from the tape for rainy and/or overcast days for points whose localization is corrected for time. The difference between column (a) and column (c) will give the temperature correction for several observation periods on each weekly strip taken separately. The last step is the linear interpolation of temperature corrections between neighbouring points of intermediate observation periods. Practical value. The proposed algorithm may help eliminate gaps in temperature data at other observation points, where the thermograph served as a backup device for recording air temperature. Research results. The measurements that fall on rainy and overcast weather are best suited for calculating thermograph corrections when air temperature changes slow down. It is necessary to identify benchmark points of time fixation, to which the observer must add the moments of putting on and removing the tape from the drum.