scholarly journals Quantitative precipitation forecast for the Brahmaputra and the Barak basins by synoptic analogue technique

MAUSAM ◽  
2021 ◽  
Vol 66 (1) ◽  
pp. 43-76
Author(s):  
SUNIT DAS ◽  
K. BHATTACHARJEE ◽  
P. ALI ◽  
M.P. LUITEL ◽  
A. CHOUDHURY ◽  
...  
Keyword(s):  
Precipitation Forecast ◽  
Quantitative Precipitation Forecast

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.

Operational Precipitation Forecast Over China Using the Weather Research and Forecasting (WRF) Model at a Gray-Zone Resolution: Impact of Convection Parameterization

2021 ◽  
Author(s):  
XU ZHANG ◽  
YUHUA YANG ◽  
BAODE CHEN ◽  
WEI HUANG
Keyword(s):  
Case Studies ◽  
Southern China ◽  
Wrf Model ◽  
Warm Season ◽  
Precipitation Forecast ◽  
Weather Research And Forecasting ◽  
Operational System ◽  
Quantitative Precipitation Forecast ◽  
Geographical Regions ◽  
Convection Parameterization

AbstractThe quantitative precipitation forecast in the 9 km operational modeling system (without the use of a convection parameterization scheme) at the Shanghai Meteorological Service (SMS) usually suffers from excessive precipitation at the grid scale and less-structured precipitation patterns. Two scale-aware convection parameterizations were tested in the operational system to mitigate these deficiencies. Their impacts on the warm-season precipitation forecast over China were analyzed in case studies and two-month retrospective forecasts. The results from case studies show that the importance of convection parameterization depends on geographical regions and weather regimes. Considering a proper magnitude of parameterized convection can produce more realistic precipitation distribution and reduce excessive grid-scale precipitation in southern China. In the northeast and southwest China, however, the convection parameterization plays an insignificant role in precipitation forecast because of strong synoptic-scale forcing. A statistical evaluation of the two-month retrospective forecasts indicates that the forecast skill for precipitation in the 9-km operational system is improved by choosing proper convection parameterization. This study suggests that improvement in contemporary convection parameterizations is needed for their usage for various meteorological conditions and reasonable partitioning between parameterized and resolved convection.

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 The hydro-meteorological chain in Piemonte region, North Western Italy - analysis of the HYDROPTIMET test cases

2005 ◽  
Vol 5 (6) ◽  
pp. 845-852 ◽  
Author(s):  
D. Rabuffetti ◽  
M. Milelli
Keyword(s):  
Numerical Models ◽  
Precipitation Forecast ◽  
Test Cases ◽  
Warning Systems ◽  
Alert System ◽  
Run Off ◽  
Quantitative Precipitation Forecast ◽  
Prediction And Prevention ◽  
Meteorological Models ◽  
North Western

Abstract. The HYDROPTIMET Project, Interreg IIIB EU program, is developed in the framework of the prediction and prevention of natural hazards related to severe hydro-meteorological events and aims to the optimisation of Hydro-Meteorological warning systems by the experimentation of new tools (such as numerical models) to be used operationally for risk assessment. The objects of the research are the mesoscale weather phenomena and the response of watersheds with size ranging from 102 to 103 km2. Non-hydrostatic meteorological models are used to catch such phenomena at a regional level focusing on the Quantitative Precipitation Forecast (QPF). Furthermore hydrological Quantitative Discharge Forecast (QDF) are performed by the simulation of run-off generation and flood propagation in the main rivers of the territory. In this way observed data and QPF are used, in a real-time configuration, for one-way forcing of the hydrological model that works operationally connected to the Piemonte Region Alert System. The main hydro-meteorological events that affected Piemonte Region in the last years are analysed, these are the HYDROPTIMET selected test cases of 14–18 November 2002 and 23–26 November 2002. The results obtained in terms of QPF and QDF offer a basis to evaluate the sensitivity of the whole hydro-meteorological chain to the uncertainties in the numerical simulations. Different configurations of non-hydrostatic meteorological models are also evaluated.

scholarly journals Probabilistic precipitation and temperature downscaling of the Twentieth Century Reanalysis over France

2016 ◽  
Vol 12 (3) ◽  
pp. 635-662 ◽  
Author(s):  
Laurie Caillouet ◽  
Jean-Philippe Vidal ◽  
Eric Sauquet ◽  
Benjamin Graff
Keyword(s):  
Climate Change ◽  
Twentieth Century ◽  
High Resolution ◽  
Large Scale ◽  
Local Scale ◽  
Precipitation Forecast ◽  
Seasonal Temperature ◽  
Downscaling Method ◽  
Quantitative Precipitation Forecast ◽  
Precipitation And Temperature

Abstract. This work proposes a daily high-resolution probabilistic reconstruction of precipitation and temperature fields in France over the 1871–2012 period built on the NOAA Twentieth Century global extended atmospheric reanalysis (20CR). The objective is to fill in the spatial and temporal data gaps in surface observations in order to improve our knowledge on the local-scale climate variability from the late nineteenth century onwards. The SANDHY (Stepwise ANalogue Downscaling method for HYdrology) statistical downscaling method, initially developed for quantitative precipitation forecast, is used here to bridge the scale gap between large-scale 20CR predictors and local-scale predictands from the Safran high-resolution near-surface reanalysis, available from 1958 onwards only. SANDHY provides a daily ensemble of 125 analogue dates over the 1871–2012 period for 608 climatically homogeneous zones paving France. Large precipitation biases in intermediary seasons are shown to occur in regions with high seasonal asymmetry like the Mediterranean. Moreover, winter and summer temperatures are respectively over- and under-estimated over the whole of France. Two analogue subselection methods are therefore developed with the aim of keeping the structure of the SANDHY method unchanged while reducing those seasonal biases. The calendar selection keeps the analogues closest to the target calendar day. The stepwise selection applies two new analogy steps based on similarity of the sea surface temperature (SST) and the large-scale 2 m temperature (T). Comparisons to the Safran reanalysis over 1959–2007 and to homogenized series over the whole twentieth century show that biases in the interannual cycle of precipitation and temperature are reduced with both methods. The stepwise subselection moreover leads to a large improvement of interannual correlation and reduction of errors in seasonal temperature time series. When the calendar subselection is an easily applicable method suitable in a quantitative precipitation forecast context, the stepwise subselection method allows for potential season shifts and SST trends and is therefore better suited for climate reconstructions and climate change studies. The probabilistic downscaling of 20CR over the period 1871–2012 with the SANDHY probabilistic downscaling method combined with the stepwise subselection thus constitutes a perfect framework for assessing the recent observed meteorological events but also future events projected by climate change impact studies and putting them in a historical perspective.

Comparison of recorded rainfall with quantitative precipitation forecast in a rainfall-runoff simulation for the Langat River Basin, Malaysia

2011 ◽  
Vol 3 (3) ◽  
Author(s):  
Lawal Billa ◽  
Hamid Assilzadeh ◽  
Shattri Mansor ◽  
Ahmed Mahmud ◽  
Abdul Ghazali
Keyword(s):  
Response Time ◽  
River Basin ◽  
Flood Forecasting ◽  
Precipitation Forecast ◽  
Limited Area ◽  
Rainfall Runoff ◽  
Runoff Simulation ◽  
Noaa Avhrr ◽  
Quantitative Precipitation Forecast ◽  
Langat River

AbstractObserved rainfall is used for runoff modeling in flood forecasting where possible, however in cases where the response time of the watershed is too short for flood warning activities, a deterministic quantitative precipitation forecast (QPF) can be used. This is based on a limited-area meteorological model and can provide a forecasting horizon in the order of six hours or less. This study applies the results of a previously developed QPF based on a 1D cloud model using hourly NOAA-AVHRR (Advanced Very High Resolution Radiometer) and GMS (Geostationary Meteorological Satellite) datasets. Rainfall intensity values in the range of 3–12 mm/hr were extracted from these datasets based on the relation between cloud top temperature (CTT), cloud reflectance (CTR) and cloud height (CTH) using defined thresholds. The QPF, prepared for the rainstorm event of 27 September to 8 October 2000 was tested for rainfall runoff on the Langat River Basin, Malaysia, using a suitable NAM rainfall-runoff model. The response of the basin both to the rainfall-runoff simulation using the QPF estimate and the recorded observed rainfall is compared here, based on their corresponding discharge hydrographs. The comparison of the QPF and recorded rainfall showed R2 = 0.9028 for the entire basin. The runoff hydrograph for the recorded rainfall in the Kajang sub-catchment showed R2 = 0.9263 between the observed and the simulated, while that of the QPF rainfall was R2 = 0.819. This similarity in runoff suggests there is a high level of accuracy shown in the improved QPF, and that significant improvement of flood forecasting can be achieved through ‘Nowcasting’, thus increasing the response time for flood early warnings.

scholarly journals RBF Neural Networks Combined with Principal Component Analysis Applied to Quantitative Precipitation Forecast for a Reservoir Watershed during Typhoon Periods

2012 ◽  
Vol 13 (2) ◽  
pp. 722-734 ◽  
Author(s):  
Chih-Chiang Wei
Keyword(s):  
Neural Networks ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Precipitation Forecast ◽  
Percentage Error ◽  
Rbf Neural Networks ◽  
Rbf Network ◽  
Quantitative Precipitation Forecast ◽  
Skill Scores

Abstract The forecast of precipitations during typhoons has received much attention in recent years. It is important in meteorology and atmospheric sciences. Hence, the study on precipitation nowcast during typhoons is of great significance to operators of a reservoir system. This study developed an improved neural network that combines the principal component analysis (PCA) technique and the radial basis function (RBF) network. The developed methodology was employed to establish the quantitative precipitation forecast model for the watershed of the Shihmen Reservoir in northern Taiwan. The results obtained from RBF, multiple linear regression (MLR), PCA–RBF, and PCA–MLR models included the forecasts of L-ahead (L = 1, 3, 6) hourly accumulated precipitations. The deducted prediction results were compared in terms of four measures [mean absolute error (MAE), RMSE, coefficient of correlation (CC), and coefficient of efficiency (CE)] and four skill scores [percentage error (PE), area-weighted error score (AWES), bias score (BIAS), and equitable threat score (ETS)]. The results showed that predictions obtained using RBF and PCA–RBF were better than those produced by MLR and PCA–MLR. Although both RBF and PCA–RBF can provide good results on average, the network architecture and the learning speed of the PCA–RBF network are superior to those of the simple RBF network. This is because PCA technique could greatly reduce the input parameters and simplify concurrently the network structure. Consequently, the PCA–RBF neural networks can be regarded as a reliable model for predicting precipitation during typhoons.

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