scholarly journals Using Betts-Miller-Janjic convective parameterization scheme in H14-31 model to forecast heavy rainfall in Vietnam

2007 ◽  
Vol 29 (2) ◽  
pp. 83-97
Author(s):  
Vu Thanh Hang ◽  
Kieu Thi Xin
Keyword(s):  
Heavy Rainfall ◽  
Precipitation Forecast ◽  
Parameterization Scheme ◽  
Model Resolution ◽  
Convective Parameterization ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Convective Adjustment ◽  
Convective Parameterization Scheme ◽  
The Tropics

According to Krishnamurti, improvements of physical parameterizations will mainly affect simulations for the tropics [10]. The study of William A. Gallus Jr. showed that the higher the model resolution and more detailed convective parameterizations, the better the skill in quantitative precipitation forecast (QPF) in general [16]. The quality of precipitation forecast is so sensitive to convective parameterization scheme (CPS) used in the model as well as model resolution. The fact shows that for high resolution regional model like H14-31 CPS based on low-level moisture convergence as Tiedtke did not give good heavy rainfall forecast in Vietnam. In this paper we used the scheme of Betts-Miller-Janjic (BMJ) based on the convective adjustment toward tropical observationally structures in reality instead of Tiedtke in Hl4-31. Statistical verification results and verification using CRA method of Hl4-31 of two CPSs for seperated cases and for three rain seasons (2003-2005) shows that heavy rainfall forecast of Hl4-31/BMJ is better than one of H14-31/TK for Vietnam-South China Sea. CRA verification also shows that it is possible to say that heavy rainfall forecast skill of l-I14-31/BMJ in tropics is nearly similar to the skill of LAPS of Australia.

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

scholarly journals Evaluation of MM5 High-Resolution Real-Time Forecasts over the Urban Area of Athens, Greece

2004 ◽  
Vol 43 (11) ◽  
pp. 1666-1678 ◽  
Author(s):  
V. Kotroni ◽  
K. Lagouvardos
Keyword(s):  
Surface Temperature ◽  
Heat Waves ◽  
Thunderstorm Activity ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Parameterization Scheme ◽  
Cold Bias ◽  
Near Surface ◽  
Convective Parameterization ◽  
Convective Parameterization Scheme

Abstract In this paper the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) forecast skill over an area of complex terrain is evaluated. Namely, the model is verified over a period of 1 yr (2002) over the greater area of Athens, Greece, for its near-surface temperature and wind forecasts, at 8- and 2-km grid spacing, but also over a 15-day period for the summer thunderstorm activity forecasts. For the near-surface temperature a cold bias is evident. The model is, in general, unable to reproduce the summer heat waves observed in the area. The increase of the grid resolution, from 8 to 2 km, results in an improvement of the forecast skill. Postprocessing of the forecasts by applying a Kalman-filtering correction method was very effective for both the 8- and the 2-km forecasts. For the forecast skill of wind, the analysis showed that there is not any net increase of the errors with increasing forecast time for the 48-h forecast period, the mean absolute errors, in general, present the lowest values at noontime, and the increase in resolution, from 8 to 2 km, results in a slight decrease of these errors. The analysis of the model skill to accurately forecast summertime precipitation showed that the 2-km simulations, without activation of the convective parameterization scheme, were unable to reproduce the observed thunderstorm activity. Sensitivity tests for the same period with simulations in which the convective parameterization was not activated for both the 8- and the 2-km simulations were still inaccurate, while activation of the convective parameterization scheme at all grids (even at 2 km) considerably increased the precipitation forecast skill.

scholarly journals Ensemble Typhoon Quantitative Precipitation Forecasts Model in Taiwan

2015 ◽  
Vol 30 (1) ◽  
pp. 217-237 ◽  
Author(s):  
Jing-Shan Hong ◽  
Chin-Tzu Fong ◽  
Ling-Feng Hsiao ◽  
Yi-Chiang Yu ◽  
Chian-You Tzeng
Keyword(s):  
Model Performance ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Model Bias ◽  
Ensemble Prediction System ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Quantitative Precipitation Forecasts ◽  
Typhoon Rainfall ◽  
Typhoon Tracks

Abstract In this study, an ensemble typhoon quantitative precipitation forecast (ETQPF) model was developed to provide typhoon rainfall forecasts for Taiwan. The ETQPF rainfall forecast is obtained by averaging the pick-out cases, which are screened using certain criterion based on given typhoon tracks from an ensemble prediction system (EPS). Therefore, the ETQPF model resembles a climatology model. However, the ETQPF model uses the quantitative precipitation forecasts (QPFs) from an EPS instead of historical rainfall observations. Two typhoon cases, Fanapi (2010) and Megi (2010), are used to evaluate the ETQPF model performance. The results show that the rainfall forecast from the ETQPF model, which is qualitatively compared and quantitatively verified, provides reasonable typhoon rainfall forecasts and is valuable for real-time operational applications. By applying the forecast track to the ETQPF model, better track forecasts lead to better ETQPF rainfall forecasts. Moreover, the ETQPF model provides the “scenario” of the typhoon QPFs according to the uncertainty of the forecast tracks. Such a scenario analysis can provide valuable information for risk assessment and decision making in disaster prevention and reduction. Deficiencies of the ETQPF model are also presented, including that the average over the pick-out case usually offsets the extremes and reduces the maximum ETQPF rainfall, the underprediction is especially noticeable for weak phase-locked rainfall systems, and the ETQPF rainfall error is related to the model bias. Therefore, reducing model bias is an important issue in further improving the ETQPF model performance.

scholarly journals Verification of precipitation forecasts of MM5 model over Epirus, NW Greece, for various convective parameterization schemes

2012 ◽  
Vol 12 (5) ◽  
pp. 1393-1405 ◽  
Author(s):  
O. A. Sindosi ◽  
A. Bartzokas ◽  
V. Kotroni ◽  
K. Lagouvardos
Keyword(s):  
Horizontal Resolution ◽  
Forecast Skill ◽  
Precipitation Forecast ◽  
Meteorological Model ◽  
Convective Parameterization ◽  
Parameterization Schemes ◽  
Mm5 Model ◽  
Convective Parameterization Scheme ◽  
High Precipitation Events ◽  
High Precipitation

Abstract. The mesoscale meteorological model MM5 is applied to 22 selected days with intense precipitation in the region of Epirus, NW Greece. At first, it was investigated whether and to what extend an increased horizontal resolution (from 8 to 2 km) improves the quantitative precipitation forecasts. The model skill was examined for the 12-h accumulated precipitation recorded at 14 meteorological stations located in Epirus and by using categorical and descriptive statistics. Then, the precipitation forecast skill for the 2 km grid was studied: (a) without and (b) with the activation of a convective parameterization scheme. From the above study, the necessity of the use of a scheme at the 2 km grid is assessed. Furthermore, three different convective parameterization schemes are compared: (a) Betts-Miller, (b) Grell and (c) Kain-Fritsch-2 in order to reveal the scheme, resulting in the best precipitation forecast skill in Epirus. Kain-Fritsch-2 and Grell give better results with the latter being the best for the high precipitation events.

scholarly journals Exceptional heavy rainfall over Ajoy, Mayurakshi and Kansabati catchments and QPF verification during flood season of September 2009

MAUSAM ◽  
2022 ◽  
Vol 63 (3) ◽  
pp. 479-488
Author(s):  
SOUMENDU SENGUPTA ◽  
B.K. MANDAL ◽  
D. PRADHAN
Keyword(s):  
Heavy Rainfall ◽  
West Bengal ◽  
Precipitation Forecast ◽  
Flood Season ◽  
Quantitative Precipitation Forecast ◽  
Damodar Valley ◽  
Average Precipitation ◽  
Synoptic Features ◽  
River Catchments

Ajoy, Mayurakshi, Kansabati are three important river catchments of West Bengal and Jharkhand state, received very heavy rainfall during two consecutive days of flood season in the month of September 2009. The contribution of heavy rainfall & combined discharges from Damodar Valley Corporation (DVC) reservoirs during the period of heavy rainspells over these catchments enhanced flood situation in some districts of West Bengal. The synoptic features based on weather charts, cloud imageries of satellite and radar pictures have been taken to analyse. The realized areal average precipitation (AAP) as per rainfall recorded at 0300 UTC of next day have also been taken to verify the quantitative precipitation forecast (QPF) of 6&7 September 2009.

scholarly journals Performance of IMD multi-model ensemble and WRF (ARW) model for sub-basin wise rainfall forecast during monsoon 2012

MAUSAM ◽  
2021 ◽  
Vol 67 (2) ◽  
pp. 323-332
Author(s):  
ASHOK KUMAR DAS ◽  
SURINDER KAUR
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
India Meteorological Department ◽  
Precipitation Forecast ◽  
False Alarms ◽  
Model Ensemble ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Arw Model ◽  
Nwp Model

The Numerical Weather Prediction models, Multi-model Ensemble (MME) (27 km × 27 km) and WRF (ARW) (9 km × 9 km) operationally run by India Meteorological Department (IMD) have been utilized to estimate sub-basin wise rainfall forecast. The sub-basin wise operational Quantitative Precipitation Forecast (QPF) have been issued by 10 field offices named Flood Meteorological Offices (FMOs) of IMD located at different flood prone areas of the country. The daily sub-basin wise NWP model rainfall forecast for 122 sub basins under these 10 FMOs for the flood season 2012 have been estimated on operational basis which are used by forecasters at FMOs as a guidance for the issue of operational sub-basin QPF for flood forecasting purposes. The performance of the MME and WRF (ARW) models rainfall at the sub-basin level have been studied in detail. The performance of WRF (ARW) and MME models is compared in the heavy rainfall case over the river basins (Mahanadi etc.) falls under FMO, Bhubaneswar and it is found that WRF (ARW) model gives better result than MME. It is also found that performance of WRF (ARW) is little better than MME when compared over all the flood prone river sub basins of India. For high rainfall categories (51-100,  >100 mm), generally these leads to floods, the success rate of model rainfall forecasts are less and false alarms are more. The NWP models are able to capture the rainfall events but there is difference in magnitudes of sub basin wise rainfall estimates.

scholarly journals Suppressing Grid-Point Storms in a Numerical Forecasting Model

Atmosphere ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1194
Author(s):  
Seung-Bu Park ◽  
Ji-Young Han
Keyword(s):  
Grid Point ◽  
Parameterization Scheme ◽  
Western Pacific Ocean ◽  
Convection Scheme ◽  
Convective Parameterization ◽  
Tropical Oceans ◽  
Test Version ◽  
Convective Parameterization Scheme ◽  
Case Simulation ◽  
The Western Pacific

The convective parameterization scheme of the Korean Integrated Model (KIM) is tentatively modified to suppress grid-point storms in the Western Pacific Ocean. The KIM v3.2.11 suffers from the numerical problem that grid-point storms degrade forecasts in the tropical oceans and around the Korean Peninsula. Another convective parameterization scheme, the new Tiedtke scheme, is implemented in the KIM. The artificial storms are suppressed in the test version because the heating and drying tendencies of the new Tiedtke scheme are stronger than those of the default KIM Simplified Arakawa-Schubert (KSAS) scheme. Based on this comparison, the KSAS scheme is modified to strengthen its heating and drying tendencies by reducing the entrainment and detrainment rates. The modified KSAS scheme suppresses grid-point storms and thus decreases grid-scale precipitation in a summertime case simulation. Twenty 10-day forecasts with the default convection scheme (KSAS) and twenty forecasts with the modified scheme are conducted and compared with each other, confirming that the modified KSAS scheme successfully suppresses grid-point storms.

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