The relationships between the upstream wind and orographic heavy rainfall in southwestern Taiwan for typhoon cases

The relationships between the upstream wind and orographic heavy rainfall in southwestern Taiwan for typhoon cases




1 Taiwan Typhoon and Flood Research Institute, 11F, No. 97, Sec. 1, Roosevelt Road, Taipei 10093, Taiwan


2 Central Weather Bureau, 64 Gongyuan Road, Taipei 10048, Taiwan

3 Department of Atmospheric Science, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan, APEC Research Center for Typhoon and Society (ACTS)


Typhoon Morakot (2009) landed on northern Taiwan and then moved toward the northwest. Extreme heavy rainfall occurred in the mountainous region of southwest Taiwan. It was noticed that there were very strong horizontal westerly flows upstream of the mountain in southwest Taiwan. The relation between this upstream horizontal westerly wind and the heavy rain over the mountain is the major focus of this study. The 24-h maximum rainfall produced by Morakot was >1500 mm, and >20 stations in the area measured rainfall >1000 mm in 24 h. An algorithm was proposed to predict the extreme orographic heavy rain over southwestern Taiwan using radar-derived low-level horizontal winds. The Chigu radar is located 80 km upstream (westerly wind) of the mountainous regions. The EVAD technique was applied to retrieve the horizontal winds. The averaged horizontal winds between 0.5 and 3.0 km height are treated as the upstream low-level flow impinging on the mountain. A very good relationship between the low-level averaged speed and the hourly rainfall amount was achieved and the linear correlation coefficient is near 0.88. A similar algorithm was applied to two other typhoons: Haitang and Talim both in 2005; linear correlation coefficients of 0.80 and 0.84 were obtained, respectively. It is suggested that the upstream velocity of the flow determined the amount of heavy rainfall over the mountainous region in the strong wind regimes.

Key words

typhoon; orographic heavy rain; Doppler radar; horizontal wind speed upstream of the mountain

Weather Radar and Hydrology

(Proceedings of a symposium held in Exeter, UK, April 2011) (IAHS Publ. 351, 2012), 293-298

Use of ensemble radar estimates of precipitation rate within a stochastic, quantitative precipitation nowcasting algorithm

Clive Pierce1, Katie Norman1 & Alan Seed2

1 Met Office, FitzRoy Road, Exeter EX1 3PB, UK


2 Australian Bureau of Meteorology, The Centre for Australian Weather and Climate Research, GPO Box 1289, Melbourne, Victoria 3001, Australia


Several techniques for the generation of ensembles of radar observations are described and evaluated. These have been combined to generate ensemble estimates of surface precipitation rate for use in conjunction with the Short Term Ensemble Prediction System. STEPS is an operational, quantitative precipitation nowcasting algorithm developed jointly by the Met Office and the Australian Bureau of Meteorology. It generates ensemble nowcasts of precipitation rate and accumulation by scale-selectively blending a weather radar-based, extrapolated analysis of surface precipitation rate with a recent precipitation forecast from a high-resolution configuration of the Unified Model, and a time series of synthetically generated precipitation fields (noise) with space–time statistical properties inferred from radar. Currently, STEPS incorporates an observation uncertainty algorithm based upon on analysis of Z-R errors. In this paper, the performance of STEPS precipitation nowcast ensembles, generated using radar ensembles, is compared with that of operational STEPS precipitation nowcasts, produced using unperturbed observations.

Key words

radar; observation error; nowcast; ensembles

Weather Radar and Hydrology

(Proceedings of a symposium held in Exeter, UK, April 2011) (IAHS Publ. 351, 2012), 299-304.

Probabilistic forecasting of rainfall from radar nowcasting and hybrid systems


University of Bristol, Department of Civil Engineering, Bristol BS8 1TR, UK



The use of Quantitative Precipitation Forecasts (QPFs) from either Numerical Weather Prediction (NWP) or radar nowcasting models in flood forecasting systems extends the time available to issue warnings and take actions. However, uncertainty in the rainfall input affects the accuracy of flow predictions. Radar nowcasts have a higher skill at short lead times, whereas NWP models produce more accurate forecasts at longer lead times. Hybrid systems, merging NWP and radar-based forecasts, have been developed to produce more skilful forecasts than either independent component (i.e. NWP/radar nowcasting). This study aims at assessing radar nowcasts and hybrid forecasts provided by the state-of-the-art model STEPS. The forecasts were run on a 1000 km  1000 km domain covering the UK, at 2-km spatial and 15-min temporal resolutions. Results show that the forecasting system benefits from the blending with the NWP forecasts.

Key words

QPFs; ensemble forecasting; STEPS; nowcasting; hybrid forecasts

Weather Radar and Hydrology

(Proceedings of a symposium held in Exeter, UK, April 2011) (IAHS Publ. 351, 2012), 305-310.

PhaSt: stochastic phase-diffusion model for ensemble rainfall nowcasting


CIMA Research Foundation, Via Magliotto 2, 17100 Savona, Italy



Hydrometeorological hazard management often requires the development of reliable statistical rainfall nowcasting systems. Ideally, such procedures should be capable of generating stochastic ensemble forecasts of precipitation intensities on scales of the order of a few kilometres, up to a few hours in advance. Ensemble rainfall nowcasting allows for characterizing the uncertainty associated with nowcasting procedures by providing a probabilistic forecast of the future evolution of an event. Here we discuss an ensemble rainfall nowcasting technique, named PhaSt (Phase Stochastic), based on the extrapolation of radar observations by a diffusive process in Fourier space. The procedure generates stochastic ensembles of precipitation intensity forecast fields where individual ensemble members can be considered as different possible realizations of the same precipitation event. The model is tested on a data set of rainfall events measured by the C-POL radar of Mt Settepani (Liguria, Italy) and its performance verified in terms of standard probabilistic scores.

Key words

nowcasting; ensemble; probabilistic forecast; rainfall

Weather Radar and Hydrology

(Proceedings of a symposium held in Exeter, UK, April 2011) (IAHS Publ. 351, 2012), 311-316.

Ensemble radar nowcasts – a multi-method approach

Alrun Tessendorf & Thomas Einfalt

Hydro & Meteo GmbH & Co. KG, Breite Straße 6-8, D-23552 Lübeck, Germany



Radar nowcasting has for a long time been a competition between individual approaches with their strengths and weaknesses. The introduction of ensembles makes it possible to benefit from several techniques and can help in forecast applications by providing statistical information. This study focuses on how to prepare results of ensemble forecasts for risk assessment in real-time warning applications. A set of ensembles, combining runs from four forecast methods with perturbed initial conditions, is constructed and the results are evaluated using six different criteria. For predicting the current forecast quality from the ensemble spreading, quality parameters based on the contingency table were derived from the ensemble forecasts.

Key words

rainfall forecast; radar; ensembles; nowcasting; risk assessment; forecast quality

Weather Radar and Hydrology

(Proceedings of a symposium held in Exeter, UK, April 2011) (IAHS Publ. 351, 2012)., 317-322

Application of Error-Ensemble prediction method to a short-term rainfall prediction model considering orographic rainfall

Eiichi Nakakita1, Tomohiro Yoshikai2 & SUNmin kim2

1 Disaster Prevention Research Institute, Kyoto University, Gokasho, Uji 611-0011, Kyoto, Japan


2 Graduate School of Engineering, Kyoto University, Kyoto-Daigaku-Katsura 615-8510, Kyoto, Japan


In order to improve the accuracy of short-term rainfall predictions, especially for orographic rainfall in mountainous regions, a conceptual approach and a stochastic approach were introduced into a radar image extrapolation using a Translation Model. In the conceptual approach, radar rainfall measurements are separated into orographic and non-orographic rain fields by solving physically-based equations, including additional atmospheric variables, such as vertical wind velocity. In the stochastic approach, mean bias of current prediction errors was estimated and used to adjust mean prediction bias. Furthermore, the vertical wind velocity was updated with the mean bias for convective rainfall. As a result, 1-h prediction accuracy in mountainous regions was much improved for the case study. In the future, improved updating procedures can be expected to allow more accurate predictions.

Key words

short-term rainfall prediction; orographic rainfall; ensemble forecasting prediction; prediction error

Weather Radar and Hydrology

(Proceedings of a symposium held in Exeter, UK, April 2011) (IAHS Publ. 351, 2012), 323-329.

On the DWD quantitative precipitation analysis and nowcasting system for real-time application in German flood risk management

TANJA WINTERRATH1, Wolfgang Rosenow2 & elmar weigl1

1 Deutscher Wetterdienst, Department of Hydrometeorology, Frankfurter Straße 135, 63067 Offenbach, Germany


2 Deutscher Wetterdienst, Department of Research and Development, Michendorfer Chaussee 23, 14473 Potsdam, Germany


Quantitative precipitation analyses and forecasts with high temporal and spatial resolution are essential for hydrological applications in the context of flood risk management. Therefore, the Deutscher Wetterdienst, together with representatives of the water management authorities of the German federal states have developed high-resolution quantitative precipitation analysis and nowcast products based on the combination of surface precipitation observations and weather radar-based precipitation estimates. Gauge adjustment is performed hourly, making use of 16 operational radar systems and approximately 1300 conventional precipitation measurement devices. The nowcast algorithm is based on the advection of precipitation elements based on the mapping of precipitation patterns in successive image data. The subsequent quantification makes use of the latest adjustment process. Additional information about the precipitation phase, required for the determination of the discharge efficiency of precipitation, is retrieved by combining various observational and model data with the radar-based forecasts. The nowcasting system is supplemented by a qualitative hail forecast.

Key words

radar; precipitation; gauge adjustment; nowcasting; quantification; precipitation phase; real time;
risk management; DWD; Germany

Weather Radar and Hydrology

(Proceedings of a symposium held in Exeter, UK, April 2011) (IAHS Publ. 351, 2012), 330-335.

Aspects of applying weather radar-based nowcasts of rainfall for highways in Denmark

M. R. Rasmussen1, S. Thorndahl1 & M. Quist2

1 Aalborg University, Department of Civil Engineering, Sohngaardsholmsvej 57, DK-9000 Aalborg, Denmark


2 Danish Road Directorate, Thomas Helstedsvej 22, DK-8660 Skanderborg, Denmark


This work investigates three different approaches to nowcasting rainfall for highways. The simplest method is based on using the observed precipitation field at the beginning of the trip. The most developed nowcast is based on a COTREC nowcaster, which is dynamically adjusted to online raingauges. The nowcasts are performed with a lead time of up to 2 h. The average speed on Danish highways varies between 110 and 130 km/h. As a result, the performance of the nowcast is dependent on the direction of the precipitation and the direction and speed of the road users, as well as the type of precipitation.

Key words

nowcast; highway; traffic conditions; weather radar

Weather Radar and Hydrology

(Proceedings of a symposium held in Exeter, UK, April 2011) (IAHS Publ. 351, 2012), 336-341.

Use of radar data in NWP-based nowcasting in the Met Office


1 Met Office, Dept of Meteorology, University of Reading, Reading RG6 6BB, UK


2 Met Office, FitzRoy Road, Exeter EX31 3PB, UK


The Met Office is developing an hourly cycling 1.5 km resolution NWP-based nowcast system
(0–6 h), principally for prediction of convective storms for flood forecasting. Test suites were run on a domain covering southern England and Wales nested in a UK 4 km domain. These have used 3D-Var or 4D-Var in combination with latent heat nudging of radar-derived precipitation rates and humidity nudging based on 3D cloud cover analyses. An example shows the precipitation forecast compared to the current extrapolation nowcast system. The results of a trial, showing positive impact of Doppler radar winds out to about 5 h on forecasts of precipitation from the 3D-Var system, are presented. The paper also discusses work underway to allow assimilation of rain-rates and radar reflectivity within the variational schemes and the potential to measure the low-level humidity impact on radar refractivity as an additional source of data to improve flood forecasting.

Key words

NWP; variational data assimilation; radar; flood forecasting; UK; nowcasting; Doppler winds; reflectivity

Weather Radar and Hydrology

(Proceedings of a symposium held in Exeter, UK, April 2011) (IAHS Publ. 351, 2012), 342-347.

Quality monitoring of UK network radars using synthesised observations from the Met Office Unified Model


1 The Met Office, Exeter, UK


2 Advanced Nowcasting Research Group, Met Office, Department of Meteorology, Univ. Reading, Reading RG6 6BB, UK


The Met Office radar processing system delivers quality-controlled radar reflectivities to NWP. Quality information and radar reflectivity data are then passed to the Observation Processing System (OPS) where synthetic observations are calculated using model fields interpolated at the exact observation locations. Long-term statistical comparison between synthetic and real observations has the advantage of identifying individual radar calibration problems through relative comparisons with other radars. The effectiveness of the forward modelling of the reflectivity can also be evaluated through absolute statistical comparisons. Presented here is an analysis of statistical information derived from the quality monitoring system. Included is a description of the contribution made to the radar signal bias with range as a result of the combined effects of the bright band, attenuation by rain and clouds and beam broadening. The results are used to demonstrate that the atmospheric gaseous attenuation makes a significant contribution to the overall range bias, and it is therefore beneficial to account for this within the radar site processing.


quality control; unified model; data assimilation; model verification; gaseous attenuation

Weather Radar and Hydrology

(Proceedings of a symposium held in Exeter, UK, April 2011) (IAHS Publ. 351, 2012), 348-353.

Operational radar refractivity retrieval for numerical weather prediction



, K. bartholemew




, A. J. Illingworth


& M. KItchen


1 University of Reading, Reading, UK


2 UK Met Office, Exeter, UK


This work describes the application of radar refractivity retrieval to the C-band radars of the UK operational weather radar network. Radar refractivity retrieval allows humidity changes near the surface to be inferred from the phase of stationary ground clutter targets. Previously, this technique had only been demonstrated for radars with klystron transmitters, for which the frequency of the transmitted signal is essentially constant. Radars of the UK operational network use magnetron transmitters which are prone to drift in frequency. The original technique has been modified to take these frequency changes into account and reliable retrievals of hourly refractivity changes have been achieved. Good correspondence has been found with surface observations of refractivity. Comparison with output of the Met Office Unified Model (UM) at 4-km resolution indicate closer agreement between the surface observations and radar-derived refractivity changes than those represented in the UM. These findings suggest that the assimilation of radar-derived refractivity changes in Numerical Weather Prediction models could help improve the representation of near-surface humidity.

Key words

radar refractivity; humidity; NWP

Weather Radar and Hydrology

(Proceedings of a symposium held in Exeter, UK, April 2011) (IAHS Publ. 351, 2012), 354-359.

Assessment of radar data assimilation in numerical rainfall forecasting on a catchment scale

jia liu1, mIchaela bray1,2 & dawei han1

1 Water and Environmental Management Research Centre, Department of Civil Engineering, University of Bristol,
Bristol BS8 1TR, UK


2 Institute of Environment and Sustainability, School of Engineering, Cardiff University, Cardiff CF24 0DE, UK


Numerical Weather Prediction (NWP) model is gaining popularity among the hydrometeorological community for rainfall forecasting. However, data assimilation of the NWP model with real-time observations, especially the weather radar data, is still a challenging problem. The NWP model has its advantage in modelling the physical processes of storm events, while its accuracy is negatively influenced by the “spin-up” effect and the errors in the model driving. To fully utilise the available information and to improve the performance of the NWP model, observations need to be assimilated in real-time. This study focuses on a small catchment located in southwest England with a drainage area of 135.2 km2. The Weather Research and Forecasting (WRF) model and the three-dimensional variational (3DVar) data assimilation system are applied for the assimilation of radar reflectivity together with surface and upper-air observations. Four 24-h storm events are selected, with variations of rainfall distribution in time and space. The improvement in rainfall forecasts caused by data assimilation is examined for four types of events. For a better assimilation, a radar correction ratio is further developed and applied to the radar data.

Key words

numerical rainfall forecasting; WRF; 3DVar data assimilation; radar reflectivity; radar bias correction

Weather Radar and Hydrology

(Proceedings of a symposium held in Exeter, UK, April 2011) (IAHS Publ. 351, 2012), 360-366.

Convective cell identification using multi-source data


Institute of Meteorology and Water Management, 40-065 Katowice, ul. Bratków 10, Poland



Identification of convective cells is an important issue for detecting severe meteorological phenomena and precipitation nowcasting. The proposed model that classifies each individual radar pixel as convective or stratiform was developed based on multi-source data and applying a fuzzy logic approach. For both classes (stratiform or convective), membership functions for all investigated parameters were defined and aggregated as weighted sums. Comparison of the weighted sums decides which category a considered radar pixel belongs to. Each membership function was determined for selected parameters from: weather radar network, satellite Meteosat 8, lightning detection system, and numerical weather prediction (NWP) model. Then convective pixels were clustered to obtain individual cells, assuming that cells with a small distance between their maxima are joined.

Key words



Weather Radar and Hydrology

(Proceedings of a symposium held in Exeter, UK, April 2011) (IAHS Publ. 351, 2012), 369-374.

Guy delrieu1, laurent bonnifait1,
pierre-emmanuel kirstetter1,2 & brice boudevillain1

1 Laboratoire d’étude des Transferts en Hydrologie et Environnement, Grenoble, France


2 National Severe Storms Laboratory, Norman, Oklahoma, USA


Characterizing the error structure of radar quantitative precipitation estimation (QPE) is recognized as a major issue for applications of radar technology in hydrological modelling. This topic is further investigated in the context of the Cevennes-Vivarais Mediterranean Hydrometeorological Observatory dedicated to improving observation and modelling of extreme hydrometeorological events in the Mediterranean. The reference rainfall problem is firstly addressed: after quality-control of the raingauge measurements, various interpolation techniques (isotropic and anisotropic Ordinary Kriging, Universal Kriging with external drift) are implemented and compared through a cross-validation procedure. Then, the block Kriging technique allows the estimation and selection of reference values for a series of time-steps
(1–12 h) and hydrological mesh sizes (5–50 km2). The conditional distributions of the residuals between radar and reference values are modelled using generalized additive models for location scale and shape. The distributions are analysed for the operational real-time radar products and the Observatory re-analysed products, the latter being by construction less affected by conditional bias. As expected, the error model is dependent on the space and time scales considered. The hourly raingauge network is found to be not dense enough for providing reliable spatial estimations for sub-daily time-steps.

Key words

Mediterranean heavy precipitation; weather radar; quantitative precipitation estimation; error model;
space and time scales

Weather Radar and Hydrology

(Proceedings of a symposium held in Exeter, UK, April 2011) (IAHS Publ. 351, 2012), 375-381.

Investigating radar relative calibration biases based on four-dimensional reflectivity comparison

bong-chul seo1, witold F. krajewski1 & james A. smith2

1 IIHR-Hydroscience & Engineering, The University of Iowa, Iowa City, Iowa 52242, USA


2 Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey 08544, USA


A methodology to compare radar reflectivity data observed from two different ground-based radars is proposed. This methodology is motivated primarily by the need to explain relative differences in radar-rainfall products and to establish sound merging procedures of multi-radar observing networks. The authors compare radar reflectivity for well-matched radar sampling volumes viewing common meteorological targets. While spatial and temporal interpolation is not performed in order to prevent any distortion arising from the averaging scheme, the authors considered temporal separation and three-dimensional matching of two different sampling volumes based on the original polar coordinates of radar observation. Since the proposed method assumes radar beam propagation under the standard atmospheric condition, we do not consider anomalous propagation cases. The reflectivity comparison results show some systematic differences year to year, but the variability of those differences is fairly large due to the sensitive nature of radar reflectivity measurement. The authors performed statistical tests to check reflectivity difference consistency for consecutive periods.

Key words

radar reflectivity; radar-rainfall; radar calibration bias

Weather Radar and Hydrology

(Proceedings of a symposium held in Exeter, UK, April 2011) (IAHS Publ. 351, 2012), 382-387.

A quality evaluation criterion for radar rain-rate data

Chulsang Yoo, Jungsoo Yoon, Jungho Kim, Cheolsoon Park &

School of Civil, Environmental and Architectural Engineering, College of Engineering, Korea University,
Seoul 136-713, Korea



This study proposed a radar rain-rate quality criterion (RRQC), a measure of goodness for the radar rain-rate. The RRQC proposed is based on the similar concept of total variance in the statistical analysis of variance, which considers both the bias and variability of radar rain-rate with respect to the raingauge rain-rate. The RRQC was estimated for three storm events with the raw radar data, along with improved versions based on G/R correction and merging by co-Kriging. Additionally, these radar data were applied to the runoff analysis of the Choongju Dam Basin, Korea. By investigating the relation between the RRQC in the rain-rate input and the errors in the runoff output, a minimum quality of radar rain-rate applicable to the rainfall–runoff analysis was explored.

Key words

radar rain-rate; RRQC; G/R ratio; co-Kriging; rainfall–runoff analysis

Weather Radar and Hydrology

(Proceedings of a symposium held in Exeter, UK, April 2011) (IAHS Publ. 351, 2012), 388-393.

Radar Quality Index (RQI) – a combined measure for beam blockage and VPR effects in a national network



1 National Severe Storms Lab, 120 David L Boren Blvd., Norman, Oklahoma 73072, USA


2 Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, 120 David L Boren Blvd, Norman, Oklahoma 73072, USA

3 Nanjing University of Information Science and Technology, Nanjing, China


The next-generation multi-sensor quantitative precipitation estimation (QPE), or “Q2”, is an experimental hydrometeorological system that integrates data from radar, raingauge, and atmospheric models and generates high-resolution precipitation products on a national scale in real-time. The quality of the Q2 radar QPE varies in space and in time due to a number of factors, which include: (1) errors in measuring radar reflectivity; (2) segregation of precipitation and non-precipitation echoes; (3) uncertainties in Z–R relationships; and (4) variability in the vertical profile of reflectivity (VPR). In the current study, a Radar QPE Quality Index (RQI) field is developed to present the radar QPE uncertainty associated with VPRs. The RQI field accounts for radar beam sampling characteristics (blockage, beam height and width) and their relationships with respect to the freezing level. A national RQI map is generated by mosaicking single radar RQI fields. The radar quality information is useful to hydrological users and can add value in radar rainfall applications.

Key words

radar QPE quality; beam blockage; VPR; national radar network

Weather Radar and Hydrology

(Proceedings of a symposium held in Exeter, UK, April 2011) (IAHS Publ. 351, 2012), 394-399.
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