the dwd quantitative precipitation nowcasting systems – a ... · the dwd quantitative...

ERAD 2012 - THE SEVENTH EUROPEAN CONFERENCE ON RADAR IN METEOROLOGY AND HYDROLOGY

The DWD Quantitative Precipitation Nowcasting Systems – A Verification Study for

Selected Flood Events Tanja Winterrath1, Thomas Reich2, Wolfgang Rosenow3, Klaus Stephan1

1Deutscher Wetterdienst, Frankfurter Straße 135, 63067 Offenbach am Main, Germany, [email protected], [email protected]

2Deutscher Wetterdienst, Lindenberger Weg 24, 13125 Berlin, Germany, [email protected] 3Deutscher Wetterdienst, Michendorfer Chaussee 23, 14473 Potsdam, Germany,

[email protected] (Dated: 29 May 2012)

Tanja Winterrath

1. Introduction

Quantitative precipitation forecasts with high temporal and spatial resolution are essential for hydrological applications in the context of flood risk management. Within the framework of the project RADVOR-OP, the Deutscher Wetterdienst (DWD) jointly with representatives of the water management authorities of the German federal states developed high-resolution quantitative precipitation forecast products applying two different approaches: first, a precipitation tracking algorithm combined with the quantitative information of gauge-adjusted, weather radar based precipitation estimates and second, the assimilation of radar analysis data into the high-resolution Numerical Weather Prediction (NWP) model COSMO-DE.

A verification study was performed comparing the forecasts of the tracking as well as the numerical approaches for selected precipitation events that led to severe flooding. Here, we introduce the key algorithms and present results of the verification focusing on the positive influence of both the quantification and the Latent Heat Nudging (LHN) on quantitative precipitation forecasts.

2. The nowcast suite

The operational weather radar network of DWD comprises 16 sites at which precipitation scans are performed every 5 minutes. The reflectivity fields measured by the 16 radars are combined to a 1 km x 1 km composite covering Germany and are transformed into amounts of precipitation by applying a categorized Z-R relationship.

These qualitative-quantitative radar composites serve as input for a radar tracking algorithm providing nowcasts for up to two hours. The tracking algorithm is based on the advection of precipitation elements using the displacement vector field that is derived from the mapping of similar precipitation structures in successive radar images. The forecast amounts of precipitation are quantified making use of the most recent gauge-adjustment procedure assuming persistence of the precipitation frequency distribution resulting in the quantitative precipitation forecasts for the next two hours with an update frequency of 15 minutes (Winterrath et al., 2011).

COSMO-DE is the high-resolution regional numerical model of DWD with a grid length of 2.8 km and a rapid update cycle of three hours. Data assimilation of the qualitative-quantitative radar composites into the NWP model COSMO-DE is realized by the method of LHN. Precipitation data are used to nudge the model into the direction of the radar-based observations by introducing latent heat into the respective grid cells (Stephan et al., 2008).

No. Date

from until N

umbe

r of

day

s

Mai

n pr

ecip

. d

ay

Federal State Weather situation

16 17/03/02 22/03/02 6 19/03/02 BW, RP Western cyclonic (Wz)

20 09/08/02 13/08/02 5 12/08/02 BY, SN, TH Trough Central Europe (TrE)

21 28/08/02 31/08/02 4 28/08/02 BW, RP, NW, TH

Fennoscandian High (HFz) followed by High Pressure Bridge Central Europe (BM).

25 12/08/04 12/08/04 1 12/08/04 NW Western cyclonic (Wz)

28 11/02/05 13/02/05 3 12/02/05 BW, BY, RP North-western cyclonic (NWz).

Tab. 1 Five selected flood events considered within the presented verification study; BW=Baden-Wuerttemberg, RP=Rhineland-Palatinate, BY=Bavaria, SN=Saxonia, TH=Thuringia, NW=North Rhine-Westphalia.


3. The flood events

The verification has been performed for five selected weather events representing different weather situations comprising stratiform as well as convective precipitation leading to severe flooding within different federal states in Germany. Table 1 gives an overview of the selected five high-impact events. In case of the numerical weather prediction model, the verification has been performed for the whole given period of the particular event, while the for the nowcasting module only the main precipitation day has been analysed.

Within this paper we present results of the event no. 20 that led to severe flooding of the river Elbe in August 2002 and the verification across all five events.

3.1 The 2002 Elbe flood

The deepening of an upper-level cut-off low across the British Isles triggered a cyclogenesis over Northern Italy propagating north-north-eastward in the following (so-called Vb-type low pressure system) while collecting warm and humid air from the Mediterranean. Figure 1 shows the mean absolute topography 500 hPa (upper left) as well as the mean surface pressure (lower left) for the five-day precipitation event. At the frontal zone to continental warm air and further enhanced through forced lifting at the northern slopes of the mountain ranges (Erzgebirge) record totals of precipitation were measured. The maximum value was observed at Zinnwald (Saxony) with 353 mm within 24 hours. Figure 1 (right) gives the 24-hour precipitation sum for the main precipitation day of the event based on interpolated gauge data clearly showing the immense precipitation amounts in the eastern part of Germany. This extreme precipitation event led to major flooding along the rivers Vltava and Elbe.

Forecast \ Observed yes no yes a = hits b = false alarms no c = misses d = correct negatives

Tab.2 Definition of the contingency table; the parameters a-d contain the number of data pairs of forecast and observation, respectively, that fulfil a specified criterion.

Fig. 1 Mean absolute topography 500 hPa (upper left) and mean surface pressure (lower left) on 09/08/2002 – 13/08/2002; precipitation sum for the main precipitation day 12/08/2002 of the flood event based on interpolated gauge

measurements (right).


4. Verification

4.1 Verification method

Verification has been performed on the basis of gauge data of the Deutscher Wetterdienst online station network. Scores have been calculated for single flood events and, in addition, a comprehensive analysis across all events has been performed in order to enhance the number of evaluable data pairs for the statistic. The definition of the scores is taken from Ebert et al.

The continuous score presented in this paper is defined as follows:

• Multiplicative Bias =

∑

∑

=

=N

ii

N

ii

ON

FN

1

1

1

1

.

According to the contingency table (Table 2) the categorical score presented in this paper is defined as follows:

• Equitable Threat Score = (a-arandom)/(a+b+c-arandom), arandom=(a+c)(a+b)/(a+b+c+d) Range: -1/3 bis 1; No Skill: 0; Perfect Score: 1.

The calculation of the scores is performed for every pixel i where either the observation Oi or the forecast Fi reaches or exceeds the threshold. In case of the number of non-trivial entries of the contingency table (a+b+c) being lower than 10, the calculation of the score is omitted and ‘NaN’ is put instead.

Fig. 3 Contingency table of the nowcast with quantification fort the Elbe flood event; legend like in Fig. 2.

Fig. 2 Contingency Table of the COSMO-DE model with LHN for the Elbe flood event: precipitation threshold (lead time) is given on the x(y)-axis; cold (warm) colours mark low (high) values.


While the Multiplicative Bias is especially suited to express the effect of the quantification of the nowcast precipitation, the Equitable Threat Score is used as the score to quantify the effect of the Latent Heat Nudging on the model forecast.

4.2 Verification of the 2002 Elbe flood precipitation forecast

Figures 2 and 3 show the contingency tables for the COSMO-DE model with LHN and for the nowcast model with quantification for different precipitation thresholds (x-axis) and lead times (y-axis). The legend of the single contingency tables is given in the upper right of the figure. The colour coding that is applied to improve the readability of the figures is defined by an equidistant division of the particular range of values with colours changing from cold for low to warm for high values.

Setting a threshold of ten non-trivial values as prerequisite for the statistical analysis the figures show that for neither model an analysis is possible for a threshold of 25 mm/h for the first and second forecast hour.

Figure 4 gives the Multiplicative Bias of the nowcast model without (left) and with (right) quantification for various thresholds (x-axis) and the first and second forecast hour (y-axis). The values show that the radar-based precipitation nowcast underestimates the intensity of the precipitation, especially for large thresholds. This is possibly due to intense attenuation of the radar beam. The quantification leads to an increase of the precipitation values and an enhancement of the multiplicative bias, however, the underestimation cannot be totally eliminated. Especially the results for the higher thresholds show a large improvement of the quantitative precipitation nowcast with values of 84% and 78% for the first and second forecast hour, respectively, for a threshold of 10 mm/h.

Figure 5 gives the Equitable Threat Score for the COSMO-DE numerical model without (left) and with (right) LHN. The assimilation of radar data into the model enhances the forecast in most of the cases. As the influence of the LHN is mainly limited to the first forecast hours, the scores for the lead time interval from 8 to 14 hours are only slightly influenced.

4.3 Verification across five selected events

Figures 6 and 7 show the contingency tables for the COSMO-DE model with LHN and for the nowcast model with quantification for different precipitation thresholds (x-axis) and lead times (y-axis). Setting a threshold of ten non-trivial values as prerequisite for the statistical analysis the figures show that for neither model an analysis is possible for a threshold of 25 mm/h for the first and second forecast hour.

Fig. 5 Equitable Threat Score of the COSMO-DE model without (left) and with (right) LHN for the Elbe flood event; legend like in Fig. 2.

Fig. 4 Multiplicative Bias of the nowcasting method without (left) and with (right) quantification; legend like in Fig. 2.


Figure 8 gives the Multiplicative Bias of the nowcast model without (left) and with (right) quantification for various thresholds (x-axis) and the first and second forecast hour (y-axis) taking all five selected cases into account. The overall values are larger than in the special case of the Elbe flood but still show the underestimation of the radar-based precipitation nowcast without quantification, especially for the larger thresholds. The quantification increases the precipitation values and enhances the multiplicative bias to values of around 80% for thresholds up to 5mm/h and 60% for a threshold of 10 mm/h. Notably, no significant difference between the first and second forecast hour can be identified for the quantitative forecast.

Figure 9 gives the Equitable Threat Score for the COSMO-DE numerical model without (left) and with (right) LHN. Taking all the five flood events into account, the assimilation of radar data into the model leads to slighter differences in the ETS than for the Elbe flood event. In summary over all lead times and thresholds, the LHN leads to an improvement of the forecast.

5. Conclusions

A comprehensive verification study has been performed for five selected precipitation events that led to severe flooding covering different regions and weather situations. The focus on the study was laid on the effect of quantification of the precipitation nowcast on the one hand and on the assimilation of radar data via Latent Heat Nudging into the DWD numerical weather forecast model COSMO-DE on the other hand. Results have been presented for the Elbe flood in 2002 and over all five flood events.

As the quantification modifies the precipitation frequency distribution, the continuous multiplicative bias is shown as an example score for the verification. For the COSMO-DE model the categorical ETS is given, because the LHN leads to a pixel-wise modification of the precipitation forecast.

Fig. 7 Contingency table of the nowcast with quantification fort the five selected flood events; legend like in Fig. 2.

Fig. 6 Contingency Table of the COSMO-DE model with LHN for the five selected flood events; legend like in Fig. 2.


For the Elbe flood event, both methods, the quantification and the LHN, lead to an improvement of the precipitation forecast. Quantification leads to an enhancement of the bias, especially for larger thresholds, while the LHN improves the ETS especially for the shorter lead times.

In the case of convective precipitation (not shown here) quantification shows no significant positive effect and even decreases the verification scores in some cases. This is due to the fact that the prerequisite of the persistence of the precipitation frequency is not fulfilled for convective precipitation. Therefore, quantification is not applied in convective weather situations.

Over the five selected cases, the quantification as well as the LHN increase the quality and quantity of the precipitation forecast and have proven to be valuable complementary forecasting tools in nowcasting and very short range forecasting for severe hydrometeorological precipitation events.

Acknowledgment

The authors like to thank the ‘Länderarbeitsgemeinschaft Wasser’ for financial support of the project RADVOR-OP.

References

Ebert E. E., et al.: Forecast Verification Issues, Methods, WWRP/WGNE Joint Working Group on Forecast Verification Research, http://www.cawcr.gov.au/projects/verification/

Stephan K., Klink S., Schraff, C., 2008: Assimilation of radar-derived rain rates into the convective-scale model COSMO-DE at DWD. Q. J. R. Meteorol. Soc., 134, 1315-1326

Winterrath T., Rosenow W., Weigl E., 2011: On the DWD quantitative precipitation analysis and nowcasting system for real-time application in German flood risk management. Weather Radar and Hydrology, IAHS Publ. 351, in press

Fig. 9 Equitable Threat Score of the COSMO-DE model without (left) and with (right) LHN for five selected flood events; legend like in Fig. 2.

Fig. 8 Multiplicative Bias of the nowcasting method without (left) and with (right) quantification LHN for five selected flood events; legend like in Fig. 2

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