METEOROLOGICAL GNSS APPLICATION FOR HEAVY RAIN ON DECEMBER 31, 2019 IN THE JAKARTA AND SURROUNDING AREAS

2021 ◽  
pp. 273
Author(s):  
Syachrul Arief ◽  
Ihsan Muhamad Muafiry
Keyword(s):  
Water Vapor ◽  
Data Processing ◽  
Average Distance ◽  
Heavy Rain ◽  
Research Area ◽  
High Rainfall ◽  
Interesting Pattern ◽  
Surrounding Areas ◽  
Gnss Data ◽  
Second Peak

This study aims to utilize GNSS for meteorology in Indonesia. With the "goGPS" software, the zenith troposphere delay (ZTD) value is estimated. Calculations in rainy conditions, the ZTD value is converted into a water vapor value (PWV). The research area for the phenomenon of heavy rain occurred at the end of 2019 in Jakarta and its surroundings, which caused flooding on January 1, 2020. According to the Geophysical Meteorology and Climatology Agency (BMKG), the flood's primary cause was high rainfall. Meanwhile, the rainfall at Taman Mini and Jatiasih stations was 335 mm/day and 260 mm/day, respectively. We get an interesting pattern of PWV values for this rain phenomenon. GNSS data processing, the PWV value at five GNSS stations around Jakarta, shows the same pattern even though the average distance between GNSS stations is ~ 30 km. The PWV value appears to increase at noon on December 30, 2019, and the peak occurs in the early hours of December 31, 2019. The PWV value suddenly decreases at noon on January 1, 2020. Next, the PWV value increases again but not as high as at the previous peak. Since January 2, 2020, the PWV value has decreased and remained almost constant until January 4, 2020. In that period, there were two events that the PWV value increased. The PWV value at the first peak is ~ 70 mm, and at the second peak ~ 65 mm. The most significant increase in PWV value was recorded at CJKT stations.

scholarly journals Landslide Susceptibility Zonation of Rongga District and Surrounding Areas Using Weight of Evidence (WoE) Method

2021 ◽  
Vol 873 (1) ◽  
pp. 012088
Author(s):  
Imam A. Sadisun ◽  
Dika B. Prasaja ◽  
Rendy D. Kartiko ◽  
Indra A. Dinata
Keyword(s):  
Landslide Susceptibility ◽  
Flow Direction ◽  
Heavy Rain ◽  
Analysis Data ◽  
Research Area ◽  
Weight Of Evidence ◽  
Validation Data ◽  
Data Set ◽  
Landslide Susceptibility Zonation ◽  
Surrounding Areas

Abstract Rongga District is located on West Bandung Regency, West Java, which is prone to landslide disaster. Morphological conditions in the form of steep hills become the one of landslide controlling factors. There are many landslide occurrences happen in this area, such as Nyomplong, Cibitung Village on March 23, 2020. The incident was triggered by heavy rain and strong winds. This area was chosen to assess the landslide susceptibility using the Weight of Evidence (WoE) Method. WoE is probabilistic bivariate method which connecting parameters causes landslide against distribution of landslide in research area. Landslide data which generated from direct observation in the field and satellite imagery morphology are 572 landslide events. The data is divided into two groups, the analysis data set (70%) and the validation data set (30%). The parameters used in the analysis are land use, slope, slope direction, curvature, elevation, rainfall, lithology, NDWI, NDVI, distance from road, distance from the river, distance to lineament, flow direction, lineament density, stream density and river density. The parameters validated by determining the value of the area under curve (AUC). AUC value> 0.6 will be used in the landslide susceptibility zonation next analysis. Validation of landslide susceptibility zonation was carried out using 172 landslide events. The result of the WoE validation shows the AUC success rate of 0.70 and AUC prediction rate of 0.76. The value of AUC shows that the modelling is good and acceptable.

Topographic amplification of crustal subsidence by the rainwater load of the 2018 heavy rain in SW Japan

2021 ◽  
Author(s):  
Syachrul Arief
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Heavy Rain ◽  
Satellite System ◽  
Rain Gauge ◽  
Precipitable Water Vapor ◽  
Surface Load ◽  
Rain Area ◽  
Movement Data ◽  
Sw Japan

<p>The huge amount of water vapor in the atmosphere caused disastrous heavy rain and floods in early July 2018 in SW Japan. Here I present a comprehensive space geodetic study of water brought by this heavy rain done by using a dense network of Global Navigation Satellite System (GNSS) receivers. </p><p>First, I reconstruct sea level precipitable water vapor above land region on the heavy rain. The total amount of water vapor derived by spatially integrating precipitable water vapor on land was ~25.8 Gt, which corresponds to the bucket size to carry water from ocean to land. I then compiled the precipitation measured with a rain radar network. The data showed the total precipitation by this heavy rain as ~22.11 Gt.</p><p>Next, I studied the crustal subsidence caused by the rainwater as the surface load. The GNSS stations located under the heavy rain area temporarily subsided 1-2 centimeters and the subsidence mostly recovered in a day. Using such vertical crustal movement data, I estimated the distribution of surface water in SW Japan. </p><p>The total amount of the estimated water load on 6 July 2018 was ~68.2 Gt, which significantly exceeds the cumulative on-land rainfalls of the heavy rain day from radar rain gauge analyzed precipitation data. I consider that such an amplification of subsidence originates from the selective deployment of GNSS stations in the concave places, e.g. along valleys and within basins, in the mountainous Japanese Islands.</p>

Intercomparison and Validation of GNSS-IWV Derived with G-Nut and Bernese Software

2017 ◽  
Author(s):  
Pawel Golaszewski ◽  
Pawel Wielgosz ◽  
Katarzyna Stepniak
Keyword(s):  
Water Vapor ◽  
Meteorological Data ◽  
Microwave Radiometer ◽  
Severe Weather ◽  
Final Solution ◽  
Weather Forecasts ◽  
Software Packages ◽  
Gnss Measurements ◽  
Gnss Data ◽  
Permanent Station

GNSS is an important source of meteorological data. GNSS measurements can provide tropospheric Zenith Wet Delays (ZWD) over wide area covered with permanent stations. In addition, when using surface synoptical data, GNSS can provide Integrated Water Vapor (IWV) which is very valuable information utilized in weather forecasts and severe weather monitoring. Hence, there is a need to test and validate various algorithms and software used for ZWD estimation. In this research, the accuracy of the ZWD estimates was tested using two different software packages: Bernese GNSS Software v.5.2 and G-Nut/Tefnut. In addition, their computational load was evaluated. The GNSS data were obtained from POTS permanent station, which is located in Potsdam, Germany. To validate the estimation results, the derived ZWD was transformed into the IWV, and afterwards compared to the reference IWV measured by the collocated Microwave Radiometer. In addition, the ZWD estimates were also compared to the EUREF final solution.

Ensemble-Based Analysis of the May 2010 Extreme Rainfall in Tennessee and Kentucky

2014 ◽  
Vol 142 (1) ◽  
pp. 222-239 ◽  
Author(s):  
Samantha L. Lynch ◽  
Russ S. Schumacher
Keyword(s):  
Mississippi River ◽  
Flash Flood ◽  
Extreme Rainfall ◽  
Heavy Rain ◽  
Heavy Precipitation ◽  
Extreme Rainfall Event ◽  
Ensemble Forecasts ◽  
Surface Cyclone ◽  
Upper Level ◽  
Surrounding Areas

Abstract From 1 to 3 May 2010, persistent heavy rainfall occurred in the Ohio and Mississippi River valleys due to two successive quasi-stationary mesoscale convective systems (MCSs), with locations in central Tennessee accumulating more than 483 mm of rain, and the city of Nashville experiencing a historic flash flood. This study uses operational global ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) to diagnose atmospheric processes and assess forecast uncertainty in this event. Several ensemble analysis methods are used to examine the processes that led to the development and maintenance of this precipitation system. Differences between ensemble members that correctly predicted heavy precipitation and those that did not were determined, in order to pinpoint the processes that were favorable or detrimental to the system's development. Statistical analysis was used to determine how synoptic-scale flows were correlated to 5-day area-averaged precipitation. The precipitation throughout Nashville and the surrounding areas occurred ahead of an upper-level trough located over the central United States. The distribution of precipitation was found to be closely related to the strength of this trough and an associated surface cyclone. In particular, when the upper-level trough was elongated, the surface cyclone remained weaker with a narrower low-level jet from the south. This caused the plume of moisture from the Caribbean Sea to be concentrated over Tennessee and Kentucky, where, in conjunction with focused ascent, heavy rain fell. Relatively small differences in the wind and pressure fields led to important differences in the precipitation forecasts and highlighted some of the uncertainties associated with predicting this extreme rainfall event.

A new approach that utilizes the GNSS atmospheric delay gradient to monitor front-type heavy rain and typhoon-type heavy rain in 2019 in Japan.

2020 ◽  
Author(s):  
Syachrul Arief ◽  
Kosuke Heki
Keyword(s):  
Water Vapor ◽  
Sea Level ◽  
Heavy Rain ◽  
Japan Meteorological Agency ◽  
Rapid Decline ◽  
Tropospheric Delay ◽  
High Time Resolution ◽  
Sea Levels ◽  
Satellite Navigation System ◽  
Grid Points

<p>We studied front-type heavy rain and typhoon-type heavy rain in 2019 in Japan, using tropospheric delay data from the dense Global Satellite Navigation System (GNSS) network GEONET. In 2019, based on data from Japan Meteorological Agency (JMA), that front type heavy rain occurred on 26-29 August 2019, and typhoon type heavy rain occurred on 10-13 October 2019.</p><p>In this study, we analyzed the behavior of water vapor during heavy rainfall, using tropospheric parameters obtained from a database at the University of Nevada, Reno (UNR). Data sets, including delays in gradient vectors in the troposphere (G), as well as delays in the zenith troposphere (ZTD), are estimated every 5 minutes. Initially, we interpolated G to get grid points. We removed the hydrostatic delay from ZTD to get zenith wet delay (ZWD). In the inversion scheme, we use G at all GEONET stations and ZWD data at low altitude GEONET stations (<100 m) as input. Then we assume that the spatial change in ZWD is proportional to G (Gx = <em>H</em> δZWD /δx, where <em>H</em> is the height of the water vapor scale) and the estimated height of sea-level ZWD at grid points throughout Japan.</p><p>We try to justify our working hypothesis that heavy rains occur when the convergence of G and ZWD sea levels is high by analyzing the hourly water vapor distribution on all days in August 2019 and October 2019. We found that both values ​​show a maximum in the period studied when two events heavy rain occurred, i.e., August 27, 2019, and October 12, 2019. Furthermore, we studied the analysis of high time resolution (every 5 minutes) on heavy rain days. The results show that the convergence of G and ZWD sea level rises before rain occurs, and ZWD shows a rapid decline once heavy rain begins.</p>

Recent development of PANDA software in GNSS data processing

2008 ◽  
Author(s):  
Chuang Shi ◽  
Qile Zhao ◽  
Jianghui Geng ◽  
Yidong Lou ◽  
Maorong Ge ◽  
...  
Keyword(s):  
Data Processing ◽  
Gnss Data

scholarly journals Validating HY-2A CMR Precipitable Water Vapor Using Ground-based and Shipborne GNSS Observations

2020 ◽  
Author(s):  
Zhilu Wu ◽  
Yanxiong Liu ◽  
Yang Liu ◽  
Jungang Wang ◽  
Xiufeng He ◽  
...  
Keyword(s):  
Water Vapor ◽  
Microwave Radiometer ◽  
Precipitable Water ◽  
Global Navigation Satellite Systems ◽  
High Temporal Resolution ◽  
Satellite Systems ◽  
The Indian Ocean ◽  
Global Navigation Satellite ◽  
Gnss Data ◽  
Gnss Observations

Abstract. The calibration microwave radiometer (CMR) onboard Haiyang-2A satellite provides wet tropospheric delays correction for altimetry data, which can also contribute to the understanding of climate system and weather processes. Ground-based Global Navigation Satellite Systems (GNSS) provide precise PWV with high temporal resolution and could be used for calibration and monitoring of the CMR data, and shipborne GNSS provides accurate PWV over open oceans, which can be directly compared with uncontaminated CMR data. In this study, the HY-2A CMR water vapor product is validated using ground-based GNSS observations of 100 IGS stations along the coastline and 56-day shipborne GNSS observations over the Indian Ocean. The processing strategy for GNSS data and CMR data is discussed in detail. Special efforts were made to the quality control and reconstruction of contaminated CMR data. The validation result shows that HY-2A CMR PWV agrees well with ground-based GNSS PWV with 2.67 mm in RMS within 100 km. Geographically, the RMS is 1.12 mm in the polar region and 2.78 mm elsewhere. The PWV agreement between HY-2A and shipborne GNSS shows a significant correlation with the distance between the ship and the satellite footprint, with an RMS of 1.57 mm for the distance threshold of 100 km. Ground-based GNSS and shipborne GNSS agree with HY-2A CMR well with no obvious system error.

scholarly journals Identification of Landslide-Prone Subsoil Using Wenner Configuration Geoelectric Method in Gayamharjo Village, Prambanan District, Sleman Regency

2019 ◽  
Vol 2 ◽  
pp. 125-129
Author(s):  
Najila Tihurua ◽  
Thaqibul Fikri Niyartama ◽  
Yunita Eri Setyaningrum ◽  
Qurrotul Uyun
Keyword(s):  
Data Processing ◽  
Research Area ◽  
Track Length ◽  
Subsurface Modeling

Landslides occur due to the field of slip. Identification of Soil Landslide Identification Using Geolistrik Method The Wenner configuration has been done in Gayamharjo Village, Prambanan Sub-district, Sleman District. This study aims to determine the structure of subsurface rocks and identify the field of ground slip in the landslide prone areas in the study area. Measurements were made as many as 3 trajectories, the smallest spaced between 20 meters electrode with 300 meters of track length. The tool used is Syscal Jr Switch-48. Data processing uses RES2DINV software that produces 2D subsurface modeling. The results of the interpretation showed that th e location of the study identified the constituent rock consisting of three layers of subsurface rocks (1,36 to 6,86) Ωm, sandstone (15,4 to 34,6) Ωm, and andesite rocks (77, 8 to 393) Ωm. In the three trajectories of the research area, there is a slip field with th e potential for landslide: track 1 at point 40 s.d.80 with a depth of 37 m, track 2 at point 220 s.d. 240 with depth 26 m, path 3 at point 100 s.d. 140 with a depth of 37 m.

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