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Novvria Sagita
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Earth & Planetary Sciences Energy Engineering Physics Public Health

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Using 3D-Var Data Assimilation for Improving the Accuracy of Initial Condition of Weather Research and Forecasting (WRF) Model in Java Region (Case Study : 23 January 2015) Sagita, Novvria; Hidayati, Rini; Hidayat, Rahmat; Gustari, Indra; Fatkhuroyan, Fatkhuroyan
Forum Geografi Vol 30, No 2 (2016): December 2016
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v30i2.2512

Abstract

Weather Research and Forecasting (WRF) is a numerical weather prediction model developed by various parties due to its open source, but the WRF has the disadvantage of low accuracy in weather prediction. One reason of low accuracy  of model is inaccuracy initial condition model to the actual atmospheric conditions. Techniques to improve the initial condition model is the observation data assimilation. In this study, we used three-dimensional variational (3D-Var) to perform data assimilation of some observation data. Observational data used in data assimilation are observation data from basic stations, non-basic stations, radiosonde data, and The Binary Universal Form for the Representation of meteorological data (BUFR) data from the National Centers for Environmental Prediction (NCEP) , and aggregate observation data from all stations. The aim of this study compares the effect of data assimilation with different data observation on January 23, 2015 at 00.00 UTC for Java island region. The results showed that changes root mean square error (RMSE) of surface temperature from 2° C to 1.7° C - 2.4° C, dew point from 2.1o C to 1.9o  C - 1.4o C, relative humidity from 16.1% to 3.5% - 14.5% after the data assimilation.
Identification of Dominant Factor for Air Pollution Fluctuations at The Beginning of Covid-19 Large-Scale Social Restrictions (PSBB) in Jakarta Sagita, Novvria; Saputra, Agung Hari; Novianti, Rima
Jurnal EnviScience (Environment Science) Vol. 6 No. 1 (2022)
Publisher : Universitas Islam Lamongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30736/jev.v6i1.334

Abstract

A Coronavirus Diseases 2019 (COVID-19) stroke many countries at the beginning of 2020. It had an impact not only in the health field but also on the environment. Some countries enforced to lockdown policy. This condition impacted to increase the air quality in big cities in the world. However, the Indonesian government ruled large scale social restriction (PSBB) since April 2020. PSBB is not as strict as lockdown in other countries because society could go out for some crucial reason such as working, and getting food. Many researchers reported that the lockdown policy decreased air pollution in some big cities such as Beijing, Italy, etc. Therefore, this study aimed to identify the dominant factor of air pollution fluctuation 1-month before PSBB, during PSBB and 1-month after PSBB. Is only PSBB reducing social mobility caused changes of air pollutions such as CO, SO2, NO2, PM, and O3 or did meteorological factors such as relative humidity and wind speed also impact air pollutions concentration? To calculate the dominant factor by highest contribution value, this study used multiplication between the slope of pollutant to relative humidity or wind speed or social mobility and the slope of relative humidity or wind speed or social mobility to the time. The result showed that the social mobility at 1-month before PSBB, during PSBB and 1-month after PSBB was the dominant factor of CO decreasing at the rate of -0.44, -0.01, and -0.11 ppm. However, the contribution of relative humidity, wind speed and social mobility to other air pollutions did not always same as the trend of air pollutions.
Perbandingan Panjang Periode Minimum Data Klimatologi Untuk Analisis IklimDengan Metode Mackus’s, n-hat Dan Multistage Nonfinite Population (MNP) Sagita, Novvria
Megasains Vol 8 No 1 (2017): Megasains Vol. 8 No. 1 Tahun 2017
Publisher : Stasiun Pemantau Atmosfer Global Bukit Kototabang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46824/megasains.v8i1.183

Abstract

Fenomena iklim tidak bisa dipungkiri menjadi salah satu faktor yang mempengaruhi kondisi pertanian di Indonesia. Analisis variabilitas iklim diperlukan untuk mengantisipasi adanya fenomena iklim esktrim yang nantinya berdampak pada produksi pangan. Analisis variabilitas iklim memerlukan data yang cukup agar memperoleh analisis yang akurat. Data yang cukup merupakan data yang memiliki panjang periode mencukupi panjang data minimum. Beberepa metode untuk menghitung panjang data minimum telah diperkenalkan diantaranya Mackus, n-hat dan Multistage Nonfinite Population (MNP). Hasil pengolahan data stasiun meterologi Lhoksumawe dari tahun 1992 hingga 2011 menggunakan ketiga metode tersebut menunjukkan hasil yang berbeda. Pengolahan data dengan metode Mackhus memperoleh hasil yang berbeda dan tidak stabil untuk panjang data minimum antar data bulanan, 3 bulanan, musiman dan tahunan, sedangkan metode n-hat dan MNP memperoleh hasil yang cenderung stabil karena tidak terpengaruh dengan jumlah populasi data.
Perbandingan Prediksi Asimilasi data Radiasi Satelit Pada Kejadian Siklon Tropis Seroja Menggunakan WRFDA CRTM dan RTTOV Kurniati, Ranti; Sagita, Novvria; Hanggoro, Wido
Jurnal Meteorologi dan Geofisika Vol. 25 No. 1 (2024)
Publisher : Pusat Penelitian dan Pengembangan BMKG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31172/jmg.v25i1.932

Abstract

Tropical cyclone prediction is essential for the process of mitigating the resulting disasters. Several numerical weather models have been developed but still produce errors in tropical cyclone predictions. Data assimilation is one method that can improve the initial condition values of numerical weather prediction models so that they can approach actual atmospheric conditions to reduce tropical cyclone prediction errors. Due to the limited meteorological parameter data used for data assimilation at the location of tropical cyclone events, most of which occur in ocean areas, satellite data is needed. Radiation data is initial data from satellite data, which is then transformed into meteorological parameter data using the radiative transfer model (RTM). The Weather Research and Forecasting (WRF) model data assimilation system (WRFDA) is an open-based numerical weather and data assimilation model that has 2 RTM options, namely the Radiative Transfer Model for TIROS Operational Vertical Sounder (RTTOV) and the Community Radiative Transfer Model (CRTM). This research uses two different RTMs to compare the prediction results of Tropical Cyclone Seroja, including minimum pressure, maximum wind speed, and trajectory. The research results show that predictions of minimum pressure, maximum wind speed, and trajectory of tropical cyclone Seroja by a numerical weather model assimilated with satellite radiation data are better than without assimilation. Furthermore, the assimilation of radiation data with RTTOV has the best accuracy in predicting the maximum wind speed and minimum pressure of tropical cyclone Seroja. Meanwhile, the assimilation of radiation data with CRTM can produce a minimum error in the trajectory of tropical cyclone Seroja. Future research requires adding satellite radiation data from various sensors and satellites.
Co-Authors Agung Hari Saputra Fatkhuroyan Fatkhuroyan, Fatkhuroyan Gustari, Indra Hariadi Hariadi Kurniati, Ranti Musyaffa, Faqih Novianti, Rima Rahmat Hidayat Rini Hidayati Wido Hanggoro