“Innovations to Support Emancipated Learning (MBKM), Research, and Community Services for Sustainable Development Goals”
141
Visualization of Aerological Diagram and Analysis
of Atmospheric Sounding Information Using Raob with Model Data During Low Visibility Conditions at Cengkareng
Meteorological Station
Bagus Primohadi Syahputra1*, Muhammad Yusuf2, Yosafat Donni Haryanto3, & Nelly Florida4
1,2,3
Department of Meteorology, School of Meteorology Climatology &
Geophysics 4Research and Development Center, Agency for Meteorology Climatology and Geophysics
*Corresponding Author: Bagus Primohadi Syahputra, bagus.primohadi38@gmail.com
Abstract
The limitations of in-situ observations of the upper air are one of the obstacles in analyzing the weather. The use of data models can be one solution. The purpose of this study was to determine the accuracy of the data model in providing upper air information using RAOB as a visualization tool for aerological diagrams and sounding information analyzers. The data used are radiosonde observation data from the Cengkareng meteorological station and 1000 – 100 mb ECMWF pressure level models at the same location as the in-situ observations.
The time chosen is when the haze and mist occur at the observation time 00 UTC for 5 events each. The method used is pearson correlation and simple visual verification. The results obtained that the correlation of the significant point plot data diagram when the mist occurs is 0.76 and when the haze occurs is 0.67 and visually as a whole show that the model data is quite close to the observation data. Correlation of 59 sounding information as a whole produces a value of 0.85 – 0.99 when the Mist occurs and a value of 0.89 – 0.99 when the Haze occurs. It is hoped that these results can be used as a consideration for the use of data models in filling in the gaps in radiosonde observation data.
Keywords: Sounding Information, RAOB, Radiosonde, ECMWF Models.
1. Introduction
Weather is a condition of the atmosphere in a limited time and space. Weather conditions are usually seen from the dynamics of the atmosphere of the surface layer and the layer above it. Observations of the upper air or upper air are carried out using pilot balloon observations (pibal) and radiosondes (Nature, 1957).
Radiosonde observations were carried out to obtain data on several weather parameters at different air altitude layers. Parameters observed were temperature, dew point, geopotential altitude (related to pressure), and wind direction and speed.
Processing radiosonde observation data will obtain various index values related to air stability and several other derived parameters which will later be very useful for analysis and weather prediction purposes (Syaifullah, 2018).
Radiosonde observations in Indonesia are generally carried out twice a day simultaneously at 00 UTC and 12 UTC. Very dynamic weather conditions make the need for up-to-date conditions of the upper air very necessary so that a model calculation appears to fill in the empty hours of observation. One model that is widely used is the ECMWF (European Center for Medium Range Weather Forecasts) model. Research related to verification of the ECMWF model for upper air parameters shows quite good performance in the tropics, especially on temperature and wind parameters (Haiden et
“Innovations to Support Emancipated Learning (MBKM), Research, and Community Services for Sustainable Development Goals”
142 al., 2021).
These parameters are common parameters that influence low visibility conditions in the lower layers of the atmosphere (Ortega et al., 2022). Low visibility conditions have a level of security risk that is quite dangerous for aviation. Aviation documents issued by the National Transportation Safety Board (NTSB) from the Federal of Aviation Administration (FAA) stated that from January 1982 - February 2005 there were a total of 58 reports consisting of 30 accidents and 28 flight incidents in the aerodrome area during the taxiing, takeoff phase. /departure, enroute and landing in the USA (Groff & Price, 2006).
Previous research related to the analysis and prediction of low visibility has been carried out using various method approaches such as the deep learning method (Ortega et al., 2022), the LSTM machine learning method (Wu et al., 2021), the NWP modeling method (NCMRWF, Unified Model , NCUM) (Singh et al., 2018) and specifically the use of RAOB in visibility applications has also been carried out by Katarina in Belgrade (Veljović et al., 2014).
However, the application of low visibility analysis and prediction in Indonesia using RAOB is still limited. So, through this research it is intended to look at the performance of the ECMWF ERA5 model on pressure level data for upper air parameters using a statistical approach and calculating the value of stability sounding information during low visibility events compared to radiosonde observation data at the BMKG meteorological station.
2. Method
This research was conducted at the observation point of the Cengkareng meteorological observation station which has routine aerial observations every day. This station serves operational meteorological activities for Soekarno Hatta International Airport which is the busiest airport in Indonesia and ranked 18th in the world in 2016 (Sugiyanto et al., 2016).
Figure 1. The research location is at the Cengkareng Meteorological Station which represents the Aerodrome area of Soekarno Hatta International Airport
The time chosen as the research reference is January 2022 with adjustments to limited radiosonde observation conditions at 00 UTC to weather conditions where low visibility events were reported, namely Mist and Haze in ME-48 encoding. The selected Haze and Mist events are 5 events each, with the selected times for Haze is being the 5th, 6th, 8th, 13th and 20th and the Mist Events on the 7th, 11th, 12th, 14th and 15th.
The data used in this study were ten radiosondes observation data divided into two
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143 low visibility conditions. Reports on weather conditions that provide information on Mist and Haze events are sourced from ME-48 Synoptic which is obtained through the CMSS internal server page. The data model used is the ECMWF ERA 5 data model which contains temperature, relative humidity, zonal and meridional component winds.
All parameters are upper air parameters differentiated by pressure layer height from 1000 to 100 mb. Model data is downloaded in NetCDF binary data format on the https://cds.climate.copernicus.eu/ page.
The data that has been collected according to the needs of this research is then processed using several supporting software as data processing tools such as Cygwin which is a terminal application based on Linux programming that is used as a tool to change file types from nc to csv. Microsoft Excel used for initial processing of model data. Notepad++ which is used as a tool to convert processing results from Microsoft Excel files into a txt format adapted for the Raob application. RAOB version 5.7 which is used as a tool to display plotting of observational data and model data in skew-T form as well as an analyzer of sounding information values.
Before adjusting the upper air decoding format in Notepad++ software, the initial preprocessing of the data model is carried out by performing basic calculations using empirical equations. The first is to find the wind direction and speed values of the components of the vector u (zonal/east-west direction) and v (meridional/north-south direction) which are already known by using vector equations. Equation 1 is the equation for calculating wind speed and Equation 2 is the equation for finding wind direction.
Where x has the same value as the u component and y has the same value as the v component (Cola GMU, 2010).
(1)
(2)
Next is to find the dew point value from the known temperature and relative humidity values using equation 3. Parameter T is the symbol for the temperature variable, Td is the dew point, RH is the relative humidity parameter, a and b are the magnus coefficients whose values are 17.625 and 243.04oC (Lawrence, 2005).
(3)
Next is to find the mixing ratio and geopotential height. The water vapor mixing ratio is defined as the ratio of the mass of water vapor to the mass of dry air in a given volume (Camuffo, 2019). This variable is expressed in equation 4,5 and 6. T is the symbol for the temperature variable, Td is the dew point, RH is the relative (Cassano, n.d.).
Geopotential height or geopotential altitude is a vertical coordinate referenced to Earth's mean sea level. This is expressed in Equation 7. Pb is static pressure (pressure at sea level) [Pa], Tb is standard temperature (temperature at sea level) [K], Lb is standard temperature lapse rate [K/m] = -0.0065 [K/m], h is height about sea level [m], hb is height at the bottom of atmospheric layer [m], R is universal gas constant = 8.31432, g0 is gravitational acceleration constant = 9.80665 and M is molar mass of Earth’s air = 0.0289644 [kg/mol]
“Innovations to Support Emancipated Learning (MBKM), Research, and Community Services for Sustainable Development Goals” 144 (Mide, n.d.).
(4)
(5)
(6)
(7)
Data that has been processed in a spreadsheet tabulation and made adjustments to the upper air code, then visualized and analyzed using RAOB 5.7. The results are verified by several methods in the next step, using Eyeball verification/visual verification and Pearson correlation statistical verification. Visual verification was carried out to see the similarities between the Skew T and the vertical wind-barbs generated from the ECMWF model compared to the radiosonde observation data (Muharsyah & Fitrianti, 2020). For the statistical correlation itself, it is carried out by calculating the times series of observation data and models on 1 type of sounding information and further correlation is carried out by looking at the relationship of all 59 variables of sounding information for each occurrence. This correlation uses the Pearson equation in equation 8 with the interpretation in Figure 2 (Selvanathan et al., 2020). (8)
Figure 2. The Scale Pearson Coefficient Correlation
3. Results and Discussion
Haze events in the time period analyzed were randomly selected on January 5 2022 to have a Skew T aerology diagram, vertical wind-barb plotting, and sounding information in Figure 3 with red indicator color descriptions to represent in situ observation data and blue indicator colors to represent ECMWF model data. Visually, the Skew T diagram shows an almost matching pattern between the model data and the observation data during the Haze incident. Some striking patterns are the changing pattern of wind shear at an altitude of 800 mb to 400 mb with a change in wind direction from the west to an east direction with an almost appropriate wind speed pattern.
Another pattern that visually shows a fairly good correlation is the vertical movement of the plot for ambient temperature data which fits very well between the model and the observations. Then on the dew point temperature data plot, the ECMWF model can also show a significant change at an altitude of 450 to 350 mb. However, a fairly different pattern occurs for heights ranging from 270 mb to 100 mb.
The mist incident in Figure 3 at the bottom also shows almost the same pattern as
“Innovations to Support Emancipated Learning (MBKM), Research, and Community Services for Sustainable Development Goals” 145 the Haze incident. The pattern here is the similarity trend between model data and visual observation data. When the Mist occurred on January 7 2022. The wind direction showed the same pattern, where the ECMWF model was able to model wind shear changes at an altitude of 550 to 400 mb with the same speed pattern. Likewise, for temperature data, the vertical pattern of the ECMWF model is able to model plot points similar to observations from the surface to a height of 100 mb. However, for the dew point temperature data, the plot pattern is only similar up to a height of 300 mb with the rest up to a height of 100 mb, the dew point temperature plot pattern looks quite different between the models and observations.
To provide further clarity regarding the relationship between areological plotting of the ECMWF model data and observations, a Pearson correlation is performed on the significant point data, at the Lifting Condensation Level (LCL - This is the height at which a parcel of air becomes saturated when it is lifted dry- adiabatically), Convective Condensation Level (CCL - This is the height to which a parcel of air, if heated sufficiently from below, will rise adiabatically until condensation starts), and Level of Free Concection (LFC - This is the height at which a parcel of air lifted dry-adiabatically until saturated (at the LCL), then lifted moist-adiabatically thereafter would first become warmer than the surrounding air) (RAOB70UserManual.Pdf, n.d.). The correlation results show that on average the combined correlation value of LCL, FCL and CCL for the Haze event has a value of 0.67 and during the Mist event the correlation value is 0.76. This can be represented according to Figure 2. The relationship between the Skew T aerology plot between the ECMWF model data and radiosonde observations has a strong and high relationship.
“Innovations to Support Emancipated Learning (MBKM), Research, and Community Services for Sustainable Development Goals” 146 Figure 3. Skew T Aerology Diagram, Vertical Wind Barb Plotting and Summary of Sounding Information during Haze (Date 5) and Mist (Date 7) events with observation data colored in
red and model data colored in blue.
Sounding information studies were also carried out to see the relationship between model data and observation data more specifically during low visibility events using the Fog FSI, Fog Threat and Fog Point indices. During the Haze incident on the 5th, radiosonde data and model data via RAOB provided analytical information in the form of the potential and occurrence rate of the formation of low visibility events from Fog.
The first index is the Fog Stability Index (FSI) where at the time of the Haze incident the observed FSI value on January 5 was 24.0 and the ecmwf FSI model was 28.8. The FSI interpretation according to RAOB guidelines shows a value of more than 55 indicates a very small Fog occurrence, a value of 31-55 indicates that Fog events can occur at an intermediate level and a value less than 31 indicates that Fog events can form with high potential. From the FSI analysis, it can be seen that the observation data can properly explain the potential for the formation of Haze according to the original state and the model data can provide the same information as radiosonde observations. This also happened well, although at a different level, where the Mist event on January 7 provided information on the FSI observation analysis at 34.4 and the model at 37.8. In a time series correlation, the FSI correlation coefficient at the time of the Haze event has a value of 0.957 and at the time of the Mist the correlation is 0.954. The interpretation shows that modeling the potential for the formation of low-visibility Haze types has a very high and very strong similarity with radiosonde observation data.
“Innovations to Support Emancipated Learning (MBKM), Research, and Community Services for Sustainable Development Goals” 147 Figure 4. Histogram Correlation of sounding information between observation data and
ecmwf model data during Haze events (Left) and Mist events (Right)
Next is Fog Point information which explains the temperature at which fog/low visibility occurs during observation along report that is released in ME-48 Synoptic Report. The observed Fog point value at the time of the Haze incident was 21.0 0C with the ecmwf model at 19.2 0C. At the time of the Mist incident, the observed fog point was measured at 22.1 0C and the model fog point was 19.5 0C. The correlation itself shows a very strong relationship at 0.83 between the fog point model and observations during the Haze event, but during the Mist event, the resulting correlation is quite weak at 0.1. The Fog Threat Index also provides information on potential threats from low visibility. The RAOB guide provides a reference threshold for the Fog Threat value if it is more than 3 then in the interpretation of a weak fog threat event, a value of 0 – 3 provides moderate fog threat information and if the fog threat value is less than 0 then the threat from low visibility is very high. During haze and mist events, both model and observation information has fog threat values that do not match the threshold and the original event, but have a fairly good correlation value with a level of 0.85 when haze occurs and 0.81 when mist occurs.
Apart from the low visibility information, in general the overall vertical of the atmosphere, the correlation of sounding information between model data and observation data is summarized in Figure 4. In total there are 59 sounding information with the division of significant point information and upper air index information.
During the Haze event, the lowest correlation was found in the WindEX index parameter of 0.10, which is information on the condition of the microburst potential in downdraft events in an unstable environment. The highest correlation is found in the 6km DCAPE parameter of 0.99, which is defined as the maximum energy available to a descending parcel. When Mist occurs, the lowest correlation is found in the NCAPE parameter with
“Innovations to Support Emancipated Learning (MBKM), Research, and Community Services for Sustainable Development Goals” 148 a value of 0. NCAPE is CAPE that is divided by the depth of the buoyancy layer, whereas the highest correlation at the time of the Mist event was in the same parameter at the time of the Haze event, namely DCAPE with a correlation of 0.98.
Correlation of the model data and the next and last observation data is through the calculation of the daily scheme with the variables analyzed are all sounding information.
During the Mist event, the correlation values on the 7th, 11th, 12th, 14th and 15th respectively were 0.99, 0.99, 0.85, 0.99 and 0.87. on average, it can be seen that the similarity of the data model is able to provide sounding information that is almost as accurate at a very high level with radiosonde observation data. Likewise, at the time of the Haze incident, the correlation values on the 5th, 6th, 8th, 13th and 21st when the low visibility incident was reported respectively were 0.99, 0.95, 0.99, 0.89, and 0.96. So the interpretation of the correlation shows that during the Haze event, the data model is also capable of modeling the same sounding information as the radiosonde observation data.
4. Conclusions
The ECMWF model data is used in analyzing and illustrating the aerology diagram during the Haze and Mist events using RAOB 5.7. This step is used as a solution to fill in and enhance the intuition of weather analysis information outside the main radiosonde observation hours. The results given by visual verification, the ECMWF model data can provide a pretty good picture of the Skew T diagram plot and almost match the observation data. The correlation itself in the plotting parameters of significant points gives values in the range of 0.67 to 0.76. Verification of the suitability of the data model was also carried out on the calculation of the low visibility information index and resulted in a correlation in the numbers 0.8 – 0.99 combined and as a whole the sounding information data, the model data has a correlation of 0.85 to 0.99 during low visibility events. Thus, it can be concluded that the model data is very good with a high to very high relationship in replacing observation data and filling in the radiosonde observation gaps as well as for weather analysis at any location independent of the main meteorological station observation location.
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