scholarly journals Coupling ensemble weather predictions based on TIGGE database with Grid-Xinanjiang model for flood forecast

2011 ◽  
Vol 29 ◽  
pp. 61-67 ◽  
Author(s):  
H.-J. Bao ◽  
L.-N. Zhao ◽  
Y. He ◽  
Z.-J. Li ◽  
F. Wetterhall ◽  
...  
Keyword(s):  
Weather Prediction ◽  
Grid Cell ◽  
Flood Forecasting ◽  
False Alarms ◽  
Flood Forecast ◽  
Distributed Hydrological Model ◽  
Xinanjiang Model ◽  
Numerical Weather ◽  
Number Of False Alarms ◽  
Promising Tool

Abstract. The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a new opportunity for flood forecast. The Grid-Xinanjiang distributed hydrological model, which is based on the Xinanjiang model theory and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km2) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the Grid-Xinanjiang model and the TIGGE database gives a promising tool for an early warning of flood events several days ahead.

scholarly journals Ensemble flood forecasting based on ensemble NWP and the GMKHM distributed hydrological model

2018 ◽  
Vol 246 ◽  
pp. 01108
Author(s):  
Lili Wang ◽  
Hongjun Bao
Keyword(s):  
Overland Flow ◽  
Hydrological Model ◽  
Weather Prediction ◽  
Flood Forecasting ◽  
False Alarms ◽  
Generation Model ◽  
Flood Forecast ◽  
Distributed Hydrological Model ◽  
Runoff Generation ◽  
Numerical Weather

The incorporation of numerical weather predictions (NWP) into a flood forecasting system can increase forecast lead times from a few hours to a few days. A single NWP forecast from a single forecast centre, however, is insufficient as it involves considerable non-predictable uncertainties and lead to a high number of false alarms. The availability of global ensemble numerical weather prediction systems through the THORPEX Interactive Grand Global Ensemble’ (TIGGE) offers a new opportunity for flood forecast. The GMKHM distributed hydrological model, which is based on a mixed runoff generation model and overland flow routing model based on kinematic wave theory, and the topographical information of each grid cell extracted from the Digital Elevation Model (DEM), is coupled with ensemble weather predictions based on the TIGGE database (CMC, CMA, ECWMF, UKMO, NCEP) for flood forecast. This paper presents a case study using the coupled flood forecasting model on the Xixian catchment (a drainage area of 8826 km2) located in Henan province, China. A probabilistic discharge is provided as the end product of flood forecast. Results show that the association of the GMKHM model and the TIGGE database gives a promising tool for the anticipation of flood events several days ahead,, comparable with that driven by raingauge observation.

scholarly journals A service-oriented architecture for ensemble flood forecast from numerical weather prediction

2015 ◽  
Vol 527 ◽  
pp. 933-942 ◽  
Author(s):  
Haiyun Shi ◽  
Tiejian Li ◽  
Ronghua Liu ◽  
Ji Chen ◽  
Jiaye Li ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Service Oriented Architecture ◽  
Weather Prediction ◽  
Flood Forecast ◽  
Numerical Weather ◽  
Service Oriented

scholarly journals Improvement of mesoscale numerical weather prediction WRF-ARW in the Republic of Belarus by assimilation of radar wind and reflectivity data

2020 ◽  
pp. 3-13
Author(s):  
Palina A. Zaiko ◽  
Aliaksandr N. Krasouski ◽  
Siarhei K. Barodka
Keyword(s):  
Mesoscale Model ◽  
Forecast Error ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Wrf Model ◽  
Radar Data ◽  
False Alarms ◽  
Strong Wind ◽  
Number Of False Alarms ◽  
Circulation Types

The forecasts of severe weather events obtained with the WRF numerical mesoscale model with the adapted system for assimilation of reflectivity and radial velocity data from the network of Belarusian Doppler weather radars used in Belhydromet in 2019 are analysed. A description of the system for the echo quality control based on the radar dual-polarisation characteristics and the method for three-dimensional variational assimilation (3D-VAR) used to assimilate data in the WRF model are described. The results of case studies on the simulation of precipitation and strong wind for various circulation types in Belarus with and without radar data assimilation are given. The statistical and object-oriented verification of these forecasts is provided. The results of the comprehensive assessment reveal a decrease in the forecast error for 10-m wind speed for the early forecast hours (+6 h) by 1.34 m/s, as well as a more accurate forecast of the location, orientation of the cloud systems and precipitation zones, and a decrease in the number of false alarms in the version with assimilation. A preliminary conclusion on the possibility of using the forecast results in nowcasting systems is also made.

scholarly journals HESS Opinions "On forecast (in)consistency in a hydro-meteorological chain: curse or blessing?"

2011 ◽  
Vol 15 (7) ◽  
pp. 2391-2400 ◽  
Author(s):  
F. Pappenberger ◽  
H. L. Cloke ◽  
A. Persson ◽  
D. Demeritt
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Turning Points ◽  
Flood Forecasting ◽  
Lead Times ◽  
Decision Framework ◽  
Numerical Weather ◽  
Undesirable Property

Abstract. Flood forecasting increasingly relies on numerical weather prediction forecasts to achieve longer lead times. One of the key difficulties that is emerging in constructing a decision framework for these flood forecasts is what to dowhen consecutive forecasts are so different that they lead to different conclusions regarding the issuing of warnings or triggering other action. In this opinion paper we explore some of the issues surrounding such forecast inconsistency (also known as "Jumpiness", "Turning points", "Continuity" or number of "Swings"). In thsi opinion paper we define forecast inconsistency; discuss the reasons why forecasts might be inconsistent; how we should analyse inconsistency; and what we should do about it; how we should communicate it and whether it is a totally undesirable property. The property of consistency is increasingly emerging as a hot topic in many forecasting environments.

Flood Forecasting Module of the Distributed Hydrological Model EasyDHM

2012 ◽  
Vol 7 (5) ◽  
pp. 534-539
Author(s):  
Xiaohui Lei ◽  
◽  
Weihong Liao ◽  
Yunzhong Jiang ◽  
Hao Wang
Keyword(s):  
Hydrological Model ◽  
Flood Forecasting ◽  
Flood Forecast ◽  
Distributed Hydrological Model ◽  
Hydrological Simulation ◽  
New Techniques ◽  
Weather Information ◽  
Adaptive Interpolation ◽  
Operational Management ◽  
Simulation Results

A flood forecasting module of the independentlydeveloped distributed hydrological model EasyDHM is developed mainly aiming to support the flood operational management. In this flood forecasting module, the accuracy of hydrological simulation is the most important task. In order to increase accuracy, some new techniques such as the management of initial parameters, management and adaptive interpolation of realtime weather information data, autocalibration of parameters, real-time flood correction, and multimodel combination techniques are introduced to this module. The module is then applied to the Nen River basin in China for confirmation of results. It is revealed that the accuracy of simulation results from the flood forecasting module is obviously higher than that from regular simulation in EasyDHM, and this independent flood module is of great importance for flood forecast and management projects.

Probabilistic 0–1-h Convective Initiation Nowcasts that Combine Geostationary Satellite Observations and Numerical Weather Prediction Model Data

2015 ◽  
Vol 54 (5) ◽  
pp. 1039-1059 ◽  
Author(s):  
John R. Mecikalski ◽  
John K. Williams ◽  
Christopher P. Jewett ◽  
David Ahijevych ◽  
Anita LeRoy ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Satellite Observations ◽  
False Alarms ◽  
Learning Approaches ◽  
Convective Initiation ◽  
Numerical Weather ◽  
Probabilistic Forecasts ◽  
Independent Case

AbstractThe Geostationary Operational Environmental Satellite (GOES)-R convective initiation (CI) algorithm predicts CI in real time over the next 0–60 min. While GOES-R CI has been very successful in tracking nascent clouds and obtaining cloud-top growth and height characteristics relevant to CI in an object-tracking framework, its performance has been hindered by elevated false-alarm rates, and it has not optimally combined satellite observations with other valuable data sources. Presented here are two statistical learning approaches that incorporate numerical weather prediction (NWP) input within the established GOES-R CI framework to produce probabilistic forecasts: logistic regression (LR) and an artificial-intelligence approach known as random forest (RF). Both of these techniques are used to build models that are based on an extensive database of CI events and nonevents and are evaluated via cross validation and on independent case studies. With the proper choice of probability thresholds, both the LR and RF techniques incorporating NWP data produce substantially fewer false alarms than when only GOES data are used. The NWP information identifies environmental conditions (as favorable or unfavorable) for the development of convective storms and improves the skill of the CI nowcasts that operate on GOES-based cloud objects, as compared with when the satellite IR fields are used alone. The LR procedure performs slightly better overall when 14 skill measures are used to quantify the results and notably better on independent case study days.

scholarly journals Forecasting auroras from regional and global magnetic field measurements

2016 ◽  
Vol 5 (1) ◽  
pp. 253-262 ◽  
Author(s):  
Kirsti Kauristie ◽  
Minna Myllys ◽  
Noora Partamies ◽  
Ari Viljanen ◽  
Pyry Peitso ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Geomagnetic Storms ◽  
Weather Prediction ◽  
Time Derivative ◽  
Field Measurements ◽  
The Arctic ◽  
False Alarms ◽  
Number Of False Alarms ◽  
Probability Estimates ◽  
Global Magnetic Field

Abstract. We use the connection between auroral sightings and rapid geomagnetic field variations in a concept for a Regional Auroral Forecast (RAF) service. The service is based on statistical relationships between near-real-time alerts issued by the NOAA Space Weather Prediction Center and magnetic time derivative (dB∕dt) values measured by five MIRACLE magnetometer stations located in Finland at auroral and sub-auroral latitudes. Our database contains NOAA alerts and dB∕dt observations from the years 2002–2012. These data are used to create a set of conditional probabilities, which tell the service user when the probability of seeing auroras exceeds the average conditions in Fennoscandia during the coming 0–12 h. Favourable conditions for auroral displays are associated with ground magnetic field time derivative values (dB∕dt) exceeding certain latitude-dependent threshold values. Our statistical analyses reveal that the probabilities of recording dB∕dt exceeding the thresholds stay below 50 % after NOAA alerts on X-ray bursts or on energetic particle flux enhancements. Therefore, those alerts are not very useful for auroral forecasts if we want to keep the number of false alarms low. However, NOAA alerts on global geomagnetic storms (characterized with Kp values  >  4) enable probability estimates of  >  50 % with lead times of 3–12 h. RAF forecasts thus rely heavily on the well-known fact that bright auroras appear during geomagnetic storms. The additional new piece of information which RAF brings to the previous picture is the knowledge on typical storm durations at different latitudes. For example, the service users south of the Arctic Circle will learn that after a NOAA ALTK06 issuance in night, auroral spotting should be done within 12 h after the alert, while at higher latitudes conditions can remain favourable during the next night.

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