scholarly journals HEAVY PRECIPITATION OCCURRENCE OVER KHERSON WEATHER STATION ACCORDING TO METEOROLOGICAL OBSERVATIONS AND ERA5 REANALYSIS

Author(s):  
Slobodianyk K. L. ◽  
Semerhei-Chumachenko A. B. ◽  
Veretnova V. O.

The paper presents the results of a study of heavy precipitation in the form of rain (> 30 mm/12 h) using data from the meteorological observations and atmospheric reanalysis ERA5 at the Kherson weather station in 2005-2021.Detected that at the Kherson there were only 19 cases of heavy rainfall, which occurred only in the warm half of the year with a maximum recurrence in July. Compared to 1961-1990, the number of heavy rains of 2005-2021 increased in July and June, and decreased in August.Determined that most of the real cases of increased precipitation in Kherson are in good agreement with the results of the ERA5 reanalysis, but in almost a third of the simulation episodes did not show heavy precipitation at the Kherson coordinates or their center was shifted.Heavy rains in Kherson were formed in a field of low atmospheric pressure, with a weak northwest wind and accompanied by thunderstorms.Clarified that most episodes of heavy rainfall in Kherson in 2005-2021 are associated with the movement of southern cyclones, others formed on the southern periphery of the anticyclone in the southwestern direction of the jet stream in the troposphere.

Author(s):  
O. Skrynyk

Chornohora is the highest mountain ridge in the Ukrainian Carpathians. There are six peaks with an altitude 2000 m. a.s.l. The range is a climatic barrier for air masses along the northwest – southeast. This study is important for understanding of the physical and geographical processes in the whole region. In addition, Chornohora not sufficiently researched compared to other mountain ranges of the Carpathians. The High-Mountain Meteorological and Astronomical Observatory (HMAO) at Pip Ivan Mt. was opened on July 29, 1938. The National Institute of Meteorology (Poland) served it. Copies of the data sheets Meteorological Observations Results (pol. Wyniki Spostrzezen Meteorologicznych) from the HMAO at Pip Ivan Mt. were successfully preserved in the private collections (October 1938 – July 1939). As is characteristic of these altitudes, there was a significant dynamics of changes in weather parameters from day to day. The average monthly atmospheric pressure values were lower for the winter months than for the summer months. The average temperature at Pip Ivan for 10 months was 0.8°С. It should be noted that the total rainfall during the study period at the station was 942.5 mm. Permanent snow cover on Pip Ivan Mt. laid from December 5 to April 9. The average wind speed for 10 months was 9.7 m s-1. Before the Second World War, there was also a branch of the Astronomical Observatory of the University of Warsaw. The place is remarkable in terms of distance from light sources. However, the average cloudiness in October 1938 – July 1939 at Pip Ivan was 7.6 (on a scale of 0-10). Within 10 months, the average cloudiness less than 20% were only 18 days. This is not enough for the astronomical observatories. From the available sources, we know that during the Soviet occupation (half of 1940 to June 1941) the weather station with a wide range of research was operated at the HMAO. We did not find any original records of observations of this period in Ukrainian archives. Probably after the collapse of the USSR, data stayed at the Archives of Russia’s Federation. After the Second World War, the Observatory was abandoned and subjected to destruction of the natural environment. Nowadays the Vasyl Stefanyk Precarpathian National University and the University of Warsaw are actively restoring the building of the Observatory. The modern name of the institution is the International Scientific Center “Observatory” (ISC “Observatory”). There already works 24/7 Ukrainian-Polish mountain search and rescue service. Also the following subdivisions are planned: meteorological and astronomical observations, integrated research laboratories with a wide spectrum of research on the Earth and environment, the place of practice for students and shelter for tourists. Recently (February 22, 2019) Precarpathian University signed a grant agreement: “Adaptation of former observatory on the Pip Ivan mountain for the needs of alpine rescue service training center” which includes the installation of a modern Automatic Weather Station. Within the framework of which large-scale studies are planned to measure the following parameters: atmospheric pressure (PA), air temperature (mean – TM, minimum – TN and maximum – TX), cloud cover (CC), precipitation (RR), snow depth (SD), wind speed (WS) and direction (WDu). Based on the recorded indicators of the dry and moistened thermometer and the humidity sensor, timely and average values of water vapour pressure (VP), relative humidity (RH), dew point temperature (DWPT), vapour-pressure deficit (VPD) and as well as evapotranspiration (ET) will be calculated. Also In the Observatory solar radiation instruments are preparing to install, with a large range of investigated parameters – from sunshine duration (SUD) or albedo (AL) to the measurements of ultraviolet A (UV-A) and B (UV-B). The weather station will also include the air quality monitoring system, which will measure the chemical gases in the atmosphere (e.g. O3, SO2, Nx, CO i CO2). With the support of other institutions, the range of air quality monitoring will be expanded. The location for meteorological observations in the ISC “Observatory” is unique. In terms of scientific research, we will have continuous monitoring of atmosphere elements and of the natural environment as a whole at an altitude of over 2000 m a.s.l. Meteorological research at this station is reasonable and will be the main task of the Observatory. Also, the results of the observations will have practical application from day to day, such as timely warning of avalanche danger or as a search and rescue service. Given the great importance of this mountain ridge in terms of climate, as well as to understand the physical and geographical processes in the whole region, it is worth to use comprehensive researches of its environment. Along with the observations made in other parts of the Carpathians, it will allow us to understand better the climatic features as well as the whole environment of the Carpathian regions.


2019 ◽  
Vol 20 (5) ◽  
pp. 999-1014 ◽  
Author(s):  
Stephen B. Cocks ◽  
Lin Tang ◽  
Pengfei Zhang ◽  
Alexander Ryzhkov ◽  
Brian Kaney ◽  
...  

Abstract The quantitative precipitation estimate (QPE) algorithm developed and described in Part I was validated using data collected from 33 Weather Surveillance Radar 1988-Doppler (WSR-88D) radars on 37 calendar days east of the Rocky Mountains. A key physical parameter to the algorithm is the parameter alpha α, defined as the ratio of specific attenuation A to specific differential phase KDP. Examination of a significant sample of tropical and continental precipitation events indicated that α was sensitive to changes in drop size distribution and exhibited lower (higher) values when there were lower (higher) concentrations of larger (smaller) rain drops. As part of the performance assessment, the prototype algorithm generated QPEs utilizing a real-time estimated and a fixed α were created and evaluated. The results clearly indicated ~26% lower errors and a 26% better bias ratio with the QPE utilizing a real-time estimated α as opposed to using a fixed value as was done in previous studies. Comparisons between the QPE utilizing a real-time estimated α and the operational dual-polarization (dual-pol) QPE used on the WSR-88D radar network showed the former exhibited ~22% lower errors, 7% less bias, and 5% higher correlation coefficient when compared to quality controlled gauge totals. The new QPE also provided much better estimates for moderate to heavy precipitation events and performed better in regions of partial beam blockage than the operational dual-pol QPE.


BMJ ◽  
1970 ◽  
Vol 2 (5700) ◽  
pp. 39-39
Author(s):  
S. Miles

2021 ◽  
Author(s):  
Carla C. M. Arce ◽  
Zoe Bont ◽  
Ricardo A. R. Machado ◽  
Paulo F. Cristaldo ◽  
Matthias Erb

2021 ◽  
Author(s):  
Eva van der Kooij ◽  
Marc Schleiss ◽  
Riccardo Taormina ◽  
Francesco Fioranelli ◽  
Dorien Lugt ◽  
...  

<p>Accurate short-term forecasts, also known as nowcasts, of heavy precipitation are desirable for creating early warning systems for extreme weather and its consequences, e.g. urban flooding. In this research, we explore the use of machine learning for short-term prediction of heavy rainfall showers in the Netherlands.</p><p>We assess the performance of a recurrent, convolutional neural network (TrajGRU) with lead times of 0 to 2 hours. The network is trained on a 13-year archive of radar images with 5-min temporal and 1-km spatial resolution from the precipitation radars of the Royal Netherlands Meteorological Institute (KNMI). We aim to train the model to predict the formation and dissipation of dynamic, heavy, localized rain events, a task for which traditional Lagrangian nowcasting methods still come up short.</p><p>We report on different ways to optimize predictive performance for heavy rainfall intensities through several experiments. The large dataset available provides many possible configurations for training. To focus on heavy rainfall intensities, we use different subsets of this dataset through using different conditions for event selection and varying the ratio of light and heavy precipitation events present in the training data set and change the loss function used to train the model.</p><p>To assess the performance of the model, we compare our method to current state-of-the-art Lagrangian nowcasting system from the pySTEPS library, like S-PROG, a deterministic approximation of an ensemble mean forecast. The results of the experiments are used to discuss the pros and cons of machine-learning based methods for precipitation nowcasting and possible ways to further increase performance.</p>


Atmosphere ◽  
2018 ◽  
Vol 9 (10) ◽  
pp. 378 ◽  
Author(s):  
Channa Rodrigo ◽  
Sangil Kim ◽  
Il Jung

This study aimed to determine the predictability of the Weather Research and Forecasting (WRF) model with different model physics options to identify the best set of physics parameters for predicting heavy rainfall events during the southwest and northeast monsoon seasons. Two case studies were used for the evaluation: heavy precipitation during the southwest monsoon associated with the simultaneous onset of the monsoon, and a low pressure system over the southwest Bay of Bengal that produced heavy rain over most of the country, with heavy precipitation associated with the northeast monsoon associated with monsoon flow and easterly disturbances. The modeling results showed large variation in the rainfall estimated by the model using the various model physics schemes, but several corresponding rainfall simulations were produced with spatial distribution aligned with rainfall station data, although the amount was not estimated accurately. Moreover, the WRF model was able to capture the rainfall patterns of these events in Sri Lanka, suggesting that the model has potential for operational use in numerical weather prediction in Sri Lanka.


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