Nowcasting for New Zealand

Transcription

1 ATMOSPHERIC SCIENCE LETTERS Atmos. Sci. Let. 6: (2005) Published online in Wiley InterScience ( DOI: /asl.88 Nowcasting for New Zealand Warren Gray, 1 * Howard Larsen 2 and Alan Seed 3 1 Radar Meteorologist, NIWA, New Zealand 2 Ministry for the Environment, New Zealand 3 Hydrology Unit, Bureau of Meteorology, Melbourne, Australia *Correspondence to: Warren Gray, Radar Meteorologist, NIWA, PO Box 14901, Kilbirnie, Wellington, New Zealand. w.gray@niwa.co.nz Abstract Techniques are assessed for analysing the skill with which weather radar data can be extrapolated to provide short-term rainfall forecasts. In addition tovisual inspection, forecast skill is assessed using catchment-averaged statistics comparing analyses with rain gauge averages, forecasts with analyses and forecast river flow with measured flow. Copyright 2005 Royal Meteorological Society Received: 10 June 2004 Revised: 8 December 2004 Accepted: 8 December 2004 Keywords: Radar rainfall estimation; nowcasting; distributed hydrological models 1. Introduction 2. Data and method Often, in assessing forecast skill, there is difficulty in finding statistics that represent the errors and skill of a forecast scheme well. While a visual inspection is useful, quantitative estimates of skill such as those resulting from linear least-square fits, i.e. correlation, slope and offset, are often used, particularly when comparisons are being made between different techniques. The article uses case studies over catchments near Auckland, New Zealand. The forecasts are made using a technique that damps the small spatial scale components of the precipitation pattern, as these are also the short-lived scales. At the same time, the remaining, larger-scale components are advected with a storm-scale motion. In addition to visual inspection, forecast skill is assessed using catchmentaveraged statistics comparing analyses with rain gauge averages, forecasts with analyses and forecast river flow with measured flow. Tables of lagged statistics are also produced to highlight timing errors. Results from the techniques show that linear leastsquares correlation statistics indicate a high level of skill in forecasting the temporal evolution of catchment-averaged rainfall. However, the visual inspection showed up errors in the renormalisation of the forecasts, and the lagged statistics showed that the forecasts appeared to be moving the rain too slowly. Improvements in the nowcasting algorithm resulting from these analyses lead to a substantial improvement in forecast skill. The comparisons of the data with river flow, determined using a distributed hydrological model, show that overall the peak flow and duration is well replicated, but that the timing of the rising limb occurs too early in the radar and rain gauge based estimates. This suggests that the hydrological model could be in error. The radar data used in this research comes from the Tamahunga radar located north of Auckland. It is one of 3 C-band radars run by MetService (NZ) Ltd. They have a 0.86 beam width, undertake a volume scan once every 15 min, and have a range resolution of 2 km and a maximum range of 240 km. The nowcasting algorithm being assessed is the spectral prognosis algorithm developed in Australia (Seed, 2003). The forecasts are made using a technique that decays the small spatial scale components of the precipitation pattern, as these are also the short-lived scales. At the same time, the remaining, larger-scale, components are advected with a field of motion vectors determined by space-lagged correlations. The term spectral prognosis has been coined to encompass this technique. Results are reported for a case study of a broad-scale rain band that passed over the Mahurangi catchment on 8 October 1999, resulting in around 50-mm accumulation in 12 h. 3. Visual inspection Results of individual cases, at individual times, can be compared with their corresponding analyses. Figure 1 shows such a comparison for a rain event over Auckland. From this, it can be seen that the nowcasting algorithm has overestimated the intensity of the rainfall at 15 min, although the later forecasts are less intense. More difficult to assess, particularly for this slow moving band, are the errors in timing. There is a suggestion that the forecast motion may have been slow compared to the actual motion. The limitations of this approach are that, while it is useful to inspect individual forecast, an assessment of Copyright 2005 Royal Meteorological Society

2 36 W. Gray et al. Figure 1. Analysis (left) and nowcast (right) of rainfall starting at 1800 UTC 7 October Nowcast lead times are 0, 15, 60 and 120 min the 100 forecasts made during a day-long event is too time consuming, and will not produce a quantitative estimate of skill. 4. Correlation, offset and slope statistics Averaging the data onto a catchment, and then looking at the statistics of the resulting time series can show quantitatively the skill/errors of a particular event. The statistics used here are from linear least-squares regression of the forecasts against the corresponding analysis. The correlation (or explained variance) indicates the degree of agreement in the patterns of the times series, and the bias and offset show the quantitative relationships. Table I shows an example of the use of simple statistics to highlight the enhanced intensity of the 15- min forecasts (slope = 1.15). Table II shows a matrix of correlations of the analysis against various time-lagged nowcasts. If the forecasts were moving the rain at the ideal speed, then the correlations along the diagonal (shown in italics) would be the highest. The numbers in bold show the maximum correlation of each forecast period against the analysis. Table II suggests that the forecast motion was significantly slower than that which actually occurred. This information was used to track a software fault and hence helped develop an improved algorithm. Table I. Least-squares regression statistics of the catchment-averaged time series for the 8 October 1999 event, where the analysis is compared with its associated nowcasts Explained variance Offset Slope Analysis min min min min min min min min min Comparisons with rain gauge data Comparing radar data against rain gauge data is fraught with difficulties. Radar data are averaged over an area, but are snap shots in time rain gauge data are averaged in time, but represent a point in space. The accompanying article (Gray and Larson, Radar rainfall estimation in the New Zealand context) highlights some of the resulting difficulties and presents some comparisons. The comparisons shown there use catchment-averaged statistics to reduce the effects of errors resulting from sampling. The rationale for the work presented here is that, if the radar rainfall estimates match well the catchment-averaged gauge data,

3 Nowcasting for New Zealand 37 Table II. Correlations of the analysis against the nowcasts, with various time-lags Time Correlation of analysis against lagged forecasts lag (min) Bold Best fit Underlined Correct lag Measured and Estimated Flow Flow (m3/s) Measured 6806 Rain gauge Modelled Radar analysis Modelled Radar Forecast Slow Radar Forecast Improved 0 3:00 6:00 9:00 12:00 Date 15:00 18:00 21:00 9/10/1999 0:00 Figure 2. Flow, as estimated by the hydrological model, using various radar and rain gauge inputs, and as measured by the flow recorder, for the Mahurangi catchment and the forecasts match the radar rainfall estimates, then the forecast will match the gauge estimates well. 6. Forecasting river flow Perhaps the ultimate test of a nowcast is its skill in giving flood warnings. To assess that, radar data (and nowcasts) have been used as inputs to a distributed hydrological model. The National Institute of Water and Atmospheric Research (NIWA) has implemented the TOPNET hydrological model (Ibbitt et al., 2001) for Mahurangi, a small rural catchment (8 15 km) near Auckland. This catchment is about 20 km from the radar, and therefore is in an ideal location to validate the rainfall estimates and forecasts. Figure 2 shows the output from the hydrological model, and the measured flow for comparison. The flow, as estimated by the either gauge or radar, data appears to be ahead of the measured flow, and the peak was overestimated by the gauge data. A forecast was made of the flow using radar analyses up until 0600 and the progressive forecasts ahead for another 135 min. The nowcast flow closely follows the flow profile formed from the radar analysis, showing that the improved nowcasting algorithm is forecasting the radar-estimated rainfall well. Also included, is a set of forecasts from the nowcast algorithm that moved the rain ahead too slowly. The difference between these slow forecasts and the analysis is easily apparent. The difference between the measured and estimated flow profiles is likely to result from the hydrological model. Further work is under way to improve the modelling techniques. Table III. The least-squares regression statistics for the improved nowcasting system for the event of the 8 October 1999 Explained variance Offset Slope Analysis min min min min min min min min min

4 38 W. Gray et al. 7. Example using the improved nowcasting system The above approaches led to significant improvements in the radar data quality control and forecasting systems. Table III and Table IV show a good agreement between the improved nowcasts made out to 135 min and the radar rainfall estimates analysis. The slope and offsets are small, and the correlation statistics show that over 80% of the variation in the times series of the catchment-averaged rainfall could be forecast Figure 3. Analysis (left) and nowcast (right) of rainfall starting at 1800 UTC 7 October Nowcast lead times are 0, 15, 60 and 120 min

5 Nowcasting for New Zealand 39 Table IV. Correlations of the analysis against the improved nowcasts, with various time-lags for the event of the 8 October 1999 Time Correlation of analysis against lagged forecasts lag (min) Bold Best fit Underlined Correct lag by the nowcasting system 135 min ahead. Also, there was little discernable delay in the arrival time of the forecast rainfall. Figure 3 shows that the pattern of rainfall is well forecast, even out to lead times of 120 min, and the over intensification of the short period forecasts have been eliminated. 8. Conclusion The techniques of correlation and least-square regression statistics have proved useful in assessing the skill of the nowcasting system. This system was also used to forecast stream flow using a distributed hydrological model. These flow forecasts and estimates suggest that further work is needed to improve the modelling of the catchment, but that there is potential to forecast well the flow out to the 135-min lead time tested. Acknowledgements The authors would like to thank MetService (NZ) for the radar data, the NZ Foundation for Research, Science and Technology for the funding (Contract C01X0218), and the Bureau of Meteorology (Aus.) for co-operation in using the nowcasting algorithm. References Ibbitt RP, Henderson RD, Copeland J, Wratt DS Simulating mountain runoff with meso-scale weather model rainfall estimates: a New Zealand experience. Journal of Hydrology 239: Seed AW A dynamic and spatial scaling approach to advection forecasting. Journal of Applied Meteorology 42: