







































Georgian Geographical Journal 2023, Vol.3 (2) 

 

The Performance of RegCM4.7.1 over Georgia’s Territory Using 

Two Different Configurations 

 Mariam Elizbarashvili1*, Zurab Seperteladze1, George Mikuchadze1  

Abstract 

This study utilised the Abdus Salam International Centre for Theoretical Physics (ICTP) Regional Climate Model 

version 4.7.1, adopting two different configurations. The ability of each configuration to reproduce mean and 

extreme temperatures and precipitation in different environmental conditions in Georgia was studied. The model 

conducted simulations over the time frame of 2009–2014, with a horizontal grid spacing of 12 km. The simulations 

utilised ERA5 data as boundary conditions. Both simulations encompass the whole geographical area of Georgia, 

as well as the Black and Caspian Seas. The outputs from these high-resolution simulations of mean and extreme 

precipitation and air temperature were compared with the observational data for 2010–2014 for Georgia’s 

territory. A comparison of existing weather station data with model data showed that the model with both 

configurations performed better in simulating the monthly mean and extreme values of temperature than those of 

precipitation. The biases between observed and simulated precipitation are high for both configurations as well. 

There is no significant difference between the two simulation results. However, it is also important to carry out 

further analysis and compare the results of these simulations not only with weather station data but also with 

different reanalysis data, which will allow us to perform not a point analysis but a spatial analysis over the entire 

area of the domain.  

Keywords: Observation, Model simulation, Mean and extreme, Temperature, Precipitation 

Introduction 

Regional climate modelling is recognised as an important tool for assessing the potential impacts of 

climate change at local and regional scales. It can be used to predict temperature, precipitation, or 

extreme weather events [1, 2]. 

Such information is crucial for policymakers, urban planners, and communities to make informed 

decisions about infrastructure, land use, and disaster preparedness. Therefore, high-resolution, reliable 

regional climate modelling is essential for individual countries and regions, for which, first, it is 

necessary to conduct experiments with the purpose of selecting regional model configurations. 

In this study, we apply the Regional Climate Model version 4.7.1 to the Georgian territory and 

surrounding area. The objective of this study is to evaluate the model's performance and its ability to 

reproduce observed data. 

The model's performance was assessed by conducting sensitivity experiments. The RegCM was 

configured, and parameters for physical processes such as convection, clouds, radiation, and boundary 

layers were set. Two simulations with two different settings were performed to understand how changes 

in the model physics configuration and domain size affect the results. The model simulation results 

were validated against observational data, and the ability of each configuration to reproduce mean and 

extreme temperatures and precipitation in different environmental conditions in Georgia was assessed. 

Methods and Materials 

The regional climate model (RegCM) was developed by the Abdus Salam International Centre for 

Theoretical Physics (ICTP) and has been widely used for climate simulations for different regions of 

the world [3-9]. 

In this study, RegCM4.7.1 simulations for the period of years 2009-2014 at a 12 km horizontal grid 

spacing were performed over Georgia’s territory with: 

1. Emanuel Cumulus convection scheme [10] and Explicit Nogherotto-Tompkins moisture scheme 

[11] (Emanuel 1). 

2. Emanuel Cumulus convection scheme with Explicit WSM5 moisture scheme (Emanuel 2). 

 
1 Department of Geography/Faculty of Exact and Natural Sciences, Ivane Javakhishvili Tbilisi State University, Tbilisi, 

Georgia 

* Corresponding author: mariam.elizbarashvili@tsu.ge 

 

mailto:mariam.elizbarashvili@tsu.ge


Elizbarashvili et al. Georgian Geographical Journal 2023, Vol.3 (2) 

The first simulation (Emanuel 1) was performed over the domain centered in clat = 41, clon = 43 

with 252 points in the E/W direction, 172 points in the N/S direction and 41 vertical levels. 

The second simulation was performed over the domain (Emanuel 2) centered in clat = 41.5, clon = 

41.2, with 200 points in the E/W direction, 128 points in the N/S direction, and 41 vertical levels. Both 

domains encompass Georgia’s territory, the Caucasus mountains, the full Black and Caspian Seas and 

surrounding areas. 

The Community Land Model version 4.5 (CLM) [12] exponential relaxation lateral boundary 

conditions scheme, stratocumulus clouds simulation, and the rapid radiation transfer model RRTM 

radiation scheme [13] were used for both simulations. 

The hourly ERA5 high-resolution climate data and the weekly sea surface temperature from the 

National Oceanic and Atmospheric Administration (NOAA) [14] were applied to drive the model. 

ERA5 is the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) 

atmospheric reanalysis of the global climate. 

RegCM simulations are computationally intensive, especially when using high spatial and temporal 

resolutions and access to sufficient computational resources is essential. For our simulations, we used 

The Georgian Research and Educational Networking Association GRENA’s resources. 

 

Study area 

The article discusses the findings of a study that compared weather data from various weather stations 

in different physical-geographical and climatic conditions in Georgia, including Tbilisi, Tsalka, Poti, 

and Mta Sabueti (as shown in Table 1). The study also included simulation data generated by two 

different configurations of a regional climate model. The observational data were compared to the data 

from the nearest grid point in the model simulation. 

 
Table 1. Meteorological stations 

Meteorological Station Latitude, N° Longitude, E° Elevation, m 

Tbilisi 41.7 44.83 600 

Tsalka 41.6 44.09 1482 

Mount Sabueti 42.03 43.48 1248 

Poti 42.14 41.68 3 

 
Tbilisi is situated in the eastern part of Georgia, particularly in the Tbilisi hollow. It occupies land 

on both sides of the Mtkvari River, at an elevation ranging from 380 to 600 metres a.s.l. The city has a 

dry subtropical climate. 

Tsalka is situated in the eastern part of Georgia, particularly in the Kvemo Kartli region. It is located 

in the Tsalka hollow, on the bank of the Tsalka reservoir and Khrami River. The town is at an elevation 

of 1482 mm a.s.l. and has a dry subtropical mountain climate. 

Mt. Sabueti is situated on the western slopes of the Likhi range, at the headwaters of the Chkhrimela 

River, with an elevation of 1246 m a.s.l. The Likhi range functions as a climatic barrier separating the 

western and eastern parts of Georgia. However, the border of the Kolkheti plain and the climatic-

botanical regions of eastern Georgia do not align with the watershed, but instead follow the eastern 

slopes of the range. Thus, Mt. Sabueti is situated inside the geographical area characterised by a hilly 

terrain and a humid subtropical climate.  

Poti is situated in the Kolkheti plain along the Black Sea coastline, at an elevation of 1-3 m a.s.l. This 

area is known for its humid subtropical marine climate. 

Results 

Annual air temperature 

Fig. 1 presents the annual air temperature variation according to the model simulations and 

observational data for the selected weather stations in Georgia. It seems that for all stations for both 

simulations, the model well describes the annual air temperature variation. 



Elizbarashvili et al. Georgian Geographical Journal 2023, Vol.3 (2) 

  
 

  

 Figure 1. Annual temperature variation for selected weather stations according to the model and observational data 

In Tbilisi, the observational data exceed the “Emanuel 2” simulation outputs for all years and almost 

coincide with the “Emanuel 1” simulation outputs for some years. In Tsalka, Poti and Mt. Sabueti, the 

model data for both simulations exceed the observational data for all years. 

The largest difference between the model and observational data of average annual air temperature 

is observed in Mount Sabueti and Poti, and the difference is 4.78 OC and 4.18 OC, respectively. There 

is not a large difference between the model simulations. 

  

 

12

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2010 2011 2012 2013 2014

Mean air temperature Tbilisi

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Mean air tempeature Tsalka

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Mean air temperature Poti

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Mean air temperature Mt. Sabueti

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Monthly mean air temperature Tbilisi

2010-2014

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Monthly mean air temperature Tsalka

2010-2014

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Elizbarashvili et al. Georgian Geographical Journal 2023, Vol.3 (2) 

  

Figure 2. Monthly mean air temperature for selected weather stations according to the model and observational data 

Monthly Mean Air Temperature 

Fig. 2 presents the monthly mean air temperature for Tbilisi, Tsalka, Poti and Mt. Sabueti according 

to the model and observational data for 2010-2014. The model simulation data well reflect the intra-

annual trend of the monthly mean air temperature. In Tbilisi, the model simulations and observational 

data almost coincide with each other throughout the year for both simulations. In Tsalka, the difference 

between the model simulations and observational data is highest during the winter, and the best 

agreement is during the summer and in March, April and October. 

Monthly Maximum Air Temperature 

Figure 3 shows the monthly maximum air temperature for 4 weather stations in Georgia according 

to the model and observational data for 2010-2014. The intra-annual movements of monthly maximum 

temperatures are in good agreement with each other according to the observational data and model data. 

There is no difference between the outputs of the two simulations. 

The largest difference between the monthly maximum air temperature observational data and model 

data is observed at the high mountainous station on Mt. Sabueti. This difference is relatively small in 

eastern Georgia’s high mountain station – Tsalka, in Tbilisi and in western Georgia on the Black Sea 

coast - in Poti (Fig. 3). Moreover, for Mt. Sabueti, both model simulations significantly exceed 

observational data for all months; there is no such picture for other selected stations. 

  
 

0

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Monthly mean air temperature Poti

2010-2014

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Sabueti

2010-2014

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Tbilisi 

2010-2014

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Monthly maximum air temperature

Tsalka 

2010-2014

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Elizbarashvili et al. Georgian Geographical Journal 2023, Vol.3 (2) 

  

Figure 3. Monthly maximum air temperature for weather stations according to the model and observational data, 2010-2014 

Monthly Minimum Air Temperature 

Fig. 4 shows the monthly minimum air temperature for Tbilisi, Tsalka, Poti and Mt. Sabueti weather 

stations for 2010-2014 according to the model and observational data. The intra-annual course of 

monthly minimum air temperature is in good agreement with each other according to observational data 

and model data. Both the “Emanuel 1” and “Emanuel 2” simulations depict monthly minimum air 

temperature observational data for Tbilisi well. 

  

  

Figure 4. Monthly minimum air temperature for the weather stations of Georgia according to the model and observational 

data, 2010-2014 

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Monthly maximum air temperature Poti 

2010 - 2014

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Monthly maximum air temperature

Mt. Sabueti 

2010-2014

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Monthly minimum air temperature 

Tbilisi

2010-2014

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Monthly minimum air temperature

Tsalka 

2010-2014

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Monthly minimum air temperature

Poti 

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Monthly minimum air temperature

Mt. Sabueti 

2010-2014

observational data emanuel 1

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Elizbarashvili et al. Georgian Geographical Journal 2023, Vol.3 (2) 

The largest difference between the monthly minimum air temperature observational data and the 

model data is observed for Tsalka in February - -13.55 OC, and -15.38 OC for the Emanuel 1 and 

Emanuel 2 simulations, respectively (Figure 4). 

The large difference between the monthly minimum air temperature observational data and model 

data is also observed in all months at the weather station on the Black Sea coast – Poti (Figure 6). For 

Poti, the model data exceed the observational data for both simulations. In the high mountain weather 

station of western Georgia – Mt Sabueti, the gap between the monthly minimum air temperature’s 

observational data and model data is relatively small. 

Annual precipitation 

Figure 5 shows the variation in annual precipitation according to the model and observational data 

for Tbilisi, Tsalka, Poti and Mt. Sabueti weather stations for 2010-2014. As we can see, in most cases 

of all stations, the model data differ significantly from the observational data. 

  

 

  

Figure 5. Annual precipitation variation for weather stations according to the model and observational data 

For Poti station, the Emanuel 1 and Emanuel 2 simulation outputs almost coincide with each other, 

and the observational data significantly exceed the model data for both simulations in all years (Fig. 5). 

For Mt. At the Sabueti station, the annual precipitation observational data significantly exceed the 

Emanuel 2 simulation data in all years, and the Emanuel 1 simulation data exceed the observational 

data in all years except 2011, where the Emanuel 1 and Emanuel 2 simulation outputs coincide with 

each other. 

Monthly mean precipitation 

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100

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300

2010 2011 2012 2013 2014

Mean precipitation Tbilisi

observational data emanuel 1

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Mean precipitatio Tsalka

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2010 2011 2012 2013 2014

Mean precipitation Poti

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2010 2011 2012 2013 2014

Mean precipitation

Mt. Sabueti

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Elizbarashvili et al. Georgian Geographical Journal 2023, Vol.3 (2) 

Fig. 6 presents the monthly mean precipitation for Tbilisi, Tsalka, Poti and Mt. Sabueti weather 

stations for 2010-2014. As we can see, the intra-annual movement of average monthly precipitation 

mainly differs from each other according to observational data and model data. 

  

  

Figure 6. Monthly mean precipitation according to the model and observational data for weather stations, 2010-2014 

For Tbilisi, the Emanuel 1 and Emanuel 2 simulation outputs differ from each other, although the 

model and observational data of some months coincide with each other. 

There is a significant difference in the weather station of Tsalka (1482 m) located in the high 

mountainous areas of eastern Georgia, where the model data significantly exceed the observational data 

in all months. For the Emanuel 1 simulation and for the Emanuel 2 simulation, the gap between the 

model and the observational data is relatively small, and in January and March, the Emanuel 2 

simulation data almost coincide with the observational data. For Poti station, the Emanuel 1 and 

Emanuel 2 simulation outputs coincide with each other, and the observational data significantly exceed 

the model data for both simulations in all months (Figure 6). For Mt. At the Sabueti station, the gap 

between the Emanuel 1 simulation output and observational data is larger than the gap between the 

Emanuel 2 simulation output and observational data for each month. 

Daily maximum precipitation 

Fig. 7 presents daily maximum precipitation for Tbilisi, Tsalka, Poti and Mt. Sabueti weather stations 

for 2010-2014. As we can see, the annual movement of maximum daily precipitation differs from each 

other according to observational data and model data. In Tbilisi and Poti, the daily maximum 

precipitation model data are less than the observational data. 

0

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Monthly mean precipitation  Tbilisi

2010-2014

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Sabueti

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Elizbarashvili et al. Georgian Geographical Journal 2023, Vol.3 (2) 

  

  

Figure 7. Daily maximum precipitation for Georgia’s weather stations for 2010-2014 

Discussion 

Comparing regional climate model results with observational data is a fundamental step in assessing 

the reliability and accuracy of the models. It informs our understanding of past and current climate 

conditions, helps identify model deficiencies, and supports the use of these models for making future 

climate projections and informed policy decisions. 

There is no significant difference between the model simulation results on two different size domains 

with two different configurations. A comparison of the model and observational data showed us that 

both configurations of the model describe the average and extreme values of air temperature and 

atmospheric precipitation for different weather stations with different accuracies. The discrepancy 

between the model data and the observational data is particularly obvious when it comes to 

precipitation. This can be explained by the complex terrain of Georgia and the resolution of the model, 

which leads to a significant difference between the model points and the actual locations of the 

meteorological stations [6]. Usually, the observational data from the meteorological stations are 

compared to the data in the nearest grid of the model simulation. It is important to conduct further 

analysis and compare the results of these simulations not only with the weather station data but also 

with different reanalysis data, which will allow us to conduct not a point but spatial analysis over the 

entire area of the domain. 

Conclusions 

Observational data from weather stations located in the different physical-geographical conditions 

of Georgia are compared with the results of simulations conducted with two different configurations of 

the regional climate model, which showed us that model’s both simulations reproduce better the 

monthly mean and extreme values of temperature than precipitation. 

20

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Tbilisi

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Tsalka

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observational data emanuel 1

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Maximum precipitation

Mt. Sabueti

observational data emanuel 1

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Elizbarashvili et al. Georgian Geographical Journal 2023, Vol.3 (2) 

Both simulations capture the variation in the annual, monthly mean and extreme values of air 

temperature better in the stations located in eastern Georgia – in Tbilisi and Tsalka – than in the stations 

located in western Georgia – in Poti and Mount Sabueti. 

The smallest bias between the observational and model monthly mean and extreme values of 

temperature for both simulations is in Tbilisi. 

There are many uncertainties in yearly, monthly and extreme precipitation. The biases between the 

observations and simulated precipitation are high, which may be explained by the model resolution, 

which causes a significant inconsistency between the model points and the locations of weather stations. 

Competing interests 

The authors declare that they have no competing interests. 

Authors’ contribution 

M.E., Z.S., and G. M. conceived the presented idea. G. M. performed the analytic calculations. M.E. 

and Z.S. took the lead in writing the manuscript. All authors provided critical feedback and helped to 

shape the research, analysis and manuscript. 

Acknowledgements 

This research has been supported by the Shota Rustaveli National Science Foundation of Georgia 

(SRNSFG) № FR-19-8110. 

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