Satellite radiance data assimilation for rainfall prediction in Java Region

Sagita, Novvria; Hidayati, Rini; Hidayat, Rahmat; Gustari, Indra

doi:10.1088/1755-1315/54/1/012037

Cited by 5 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Model asimilasi data satelit adalah model asimilasi yang menambahkan data radiasi suhu puncak awan dalam perhitungan model WRF (Sagita, 2017). Hasil prediksi menggambarkan adanya distribusi hujan yang lokasinya hampir serupa seperti yang digambarkan oleh hasil model lainnya dengan intensitas dan luasan yang cenderung tidak besar.…”

Section: Metodologiunclassified

Pengaruh Asimilasi Data Penginderaan Jauh (Radar Dan Satelit) Pada Prediksi Cuaca Numerik Untuk Estimasi Curah Hujan (Impact of Remote Sensing Data Assimilation (Radar and Satellite) on Numerical Weather Prediction for Rainfall Estimation)

Paski

2018

jurnal_inderaja

View full text Add to dashboard Cite

One of the main problems in numerical weather modeling was the inaccuracy of initial condition data (initial conditions). This study reinforced the influence of assimilation of remote sensing observation data on initial conditions for predictive numerical rainfall in BMKG radar area Tangerang (Province of Banten and DKI Jakarta) on January 24, 2016. The procedure applied to rainfall forecast was the Weather Research and Forecasting model (WRF) with a down-to-down multi-nesting technique from Global Forecast System (GFS) output, the model was assimilated to radar and satellite image observation data using WRF Data Assimilation (WRFDA) 3DVAR system. Data was used as preliminary data from surface observation data, EEC C-Band radar data, AMSU-A satellite sensor data and MHS sensors. The analysis was done qualitatively by looking at the measurement scale. Observation data was used to know rainfall data. The results of the study showed that producing rainfall predictions with different assimilation of data produced different predictions. In general, there were improvements in the rainfall predictions with assimilation of satellite data was showing the best results.

show abstract

Section: Metodologiunclassified

Pengaruh Asimilasi Data Penginderaan Jauh (Radar Dan Satelit) Pada Prediksi Cuaca Numerik Untuk Estimasi Curah Hujan (Impact of Remote Sensing Data Assimilation (Radar and Satellite) on Numerical Weather Prediction for Rainfall Estimation)

Paski

2018

jurnal_inderaja

View full text Add to dashboard Cite

show abstract

“…Lawrence et al [10] pointed out that microwave radiances sensitive to atmospheric temperature and humidity successfully reduced error of initial conditions for the forecast models. The direct assimilation of microwave radiance data can give a certain impact on the background filed, such as the temperature, humidity, and flow fields, consequently further improved the quantitative prediction of heavy rainfall [11][12][13]. Even for the forecasting of hurricanes, microwave radiances from polar satellites can also provide unique information on the large scale atmospheric conditions which is crucial and helpful in reducing the time and location errors of a hurricane's track [14].…”

Section: Introductionmentioning

confidence: 99%

Evaluation and Assimilation of FY-3C/D MWHS-2 Radiances in the RMAPS-ST

et al. 2022

View full text Add to dashboard Cite

Currently, humidity information can be obtained from the Microwave Humidity Sounder-2 (MWHS-2) mounted on the polar-orbiting satellites FY-3C and FY-3D. However, making full use of the MWHS-2 data remains a challenge, particularly in the application of regional numerical weather models. This study is the first to include MWHS-2 radiance data in the Rapid-refresh Multi-scale Analysis and Prediction System—Short-term (RMAPS-ST) regional model. The results and impact of MWHS-2 radiance data assimilation were investigated and evaluated. It is found that MWHS-2 radiance data can be effectively assimilated in the RMAPS-ST after a series of quality control and variational bias correction. Benefits could be obtained in the reduction of background departures for each humidity sounding channel. Assimilation experiments over a period of one month were carried out, and the impacts of MWHS-2 radiances were quantitatively analyzed on the forecasts of RMAPS-ST system. The results showed that MWHS-2 saw a small but significant improvement for low-level humidity of short-range forecast, by 16.5% and 3.2% in terms of mean bias and root-mean-square error, respectively. The positive impact on short-range forecast also can be found for middle and low level temperature and wind. For quantitative precipitation forecast, the assimilation of MWHS-2 radiances increased the score skills of different rainfall levels in the first 12 h forecast by an average of 1.4%. There was a slight overall improvement in the 24-h precipitation forecast for over-estimation and false alarm of 3-h accumulated rainfall below 1.0 mm, with 0.75% and 0.36%, respectively. The addition of MWHS-2 radiance data gives a small positive impact on low-level humidity, temperature, and wind in the RMAPS-ST regional model, and it also improves short-range forecast of rainfall, particularly in the first 12 h of the forecast.

show abstract

“…Zou et al [22] assimilated satellite radiances of microwave humidity sounding to forecast precipitation events with a new cloud detection algorithm and increased the threat scores of 3-h accumulated rainfall by about 50% after 3-6 h of the forecast range. Sagita et al [23] investigated the impact of satellite radiances from several instruments on rainfall prediction in the Java region and pointed out that a higher accuracy of precipitation forecast could be obtained with radiance data assimilation in the system, even though the contribution from satellite observations was small. Wang et al [24] suggested that the assimilation of water vapor radiances from the Advanced Himawari Imger (AHI) of Himawari-8 improved the initial wind and humidity fields in the forecast of heavy rainfall, which contributed to the accuracy of rainstorm prediction in the first 3-6 h.…”

Section: Introductionmentioning

confidence: 99%

Impacts of Assimilating ATMS Radiances on Heavy Rainfall Forecast in RMAPS-ST

et al. 2020

View full text Add to dashboard Cite

The Advanced Technology Microwave Sounder (ATMS) mounted on the Suomi National Polar-Orbiting Partnership (NPP) satellite can provide both temperature and humidity information for a weather prediction model. Based on the rapid-refresh multi-scale analysis and prediction system—short-term (RMAPS-ST), we investigated the impact of ATMS radiance data assimilation on strong rainfall forecasts. Two groups of experiments were conducted to forecast heavy precipitation over North China between 18 July and 20 July 2016. The initial conditions and forecast results from the two groups of experiments have been compared and evaluated against observations. In comparison with the first group of experiments that only assimilated conventional observations, some added value can be obtained for the initial conditions of temperature, humidity, and wind fields after assimilating ATMS radiance observations in the system. For the forecast results with the assimilation of ATMS radiances, the score skills of quantitative forecast rainfall have been improved when verified against the observed rainfall. The Heidke skill score (HSS) skills of 6-h accumulated precipitation in the 24-h forecasts were overall increased, more prominently so for the heavy rainfall above 25 mm in the 0–6 h of forecasts. Assimilating ATMS radiance data reduced the false alarm ratio of quantitative precipitation forecasting in the 0–12 h of the forecast range and thus improved the threat scores for the heavy rainfall storm. Furthermore, the assimilation of ATMS radiances improved the spatial distribution of hourly rainfall forecast with observations compared with that of the first group of experiments, and the mean absolute error was reduced in the 10-h lead time of forecasts. The inclusion of ATMS radiances provided more information for the vertical structure of features in the temperature and moisture profiles, which had an indirect positive impact on the forecasts of the heavy rainfall in the RMAPS-ST system. However, the deviation in the location of the heavy rainfall center requires future work.

show abstract

Satellite radiance data assimilation for rainfall prediction in Java Region

Cited by 5 publications

References 5 publications

Pengaruh Asimilasi Data Penginderaan Jauh (Radar Dan Satelit) Pada Prediksi Cuaca Numerik Untuk Estimasi Curah Hujan (Impact of Remote Sensing Data Assimilation (Radar and Satellite) on Numerical Weather Prediction for Rainfall Estimation)

Pengaruh Asimilasi Data Penginderaan Jauh (Radar Dan Satelit) Pada Prediksi Cuaca Numerik Untuk Estimasi Curah Hujan (Impact of Remote Sensing Data Assimilation (Radar and Satellite) on Numerical Weather Prediction for Rainfall Estimation)

Evaluation and Assimilation of FY-3C/D MWHS-2 Radiances in the RMAPS-ST

Impacts of Assimilating ATMS Radiances on Heavy Rainfall Forecast in RMAPS-ST

Contact Info

Product

Resources

About