Predictability of Heavy Precipitation in the Waikato River Basin of New Zealand

Stacey Dravitzki Victoria University of Wellington, Wellington, New Zealand

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James McGregor Victoria University of Wellington, Wellington, New Zealand

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Abstract

This paper investigates the predictability of heavy precipitation in the economically important Waikato River basin of New Zealand. A 2-yr archive of Global Forecast System (GFS) model data to +180 h for the period August 2005–August 2007 forms the basis of the study. GFS model predictions of precipitation are compared to surface measurements from 22 stations in and around the river basin. Categorical hit rate and bias, threat score, false-alarm ratio, probability of detection, RMSE, skill score, and mean error are all plotted as a function of model lead time for the 2-yr period.

The general synoptic structures of heavy precipitation events are often identified at long lead times. An example is shown for January 2006 when heavy rainfall is identified by GFS at +150 h, accurate location and timing is given at +48 h. Precipitation amounts are systematically underpredicted by GFS and the spatial distribution of rainfall is limited by model resolution. The value of GFS lies in its ability to provide early warning of potential heavy precipitation.

Three heavy rainfall events were selected for higher-resolution Weather Research and Forecasting (WRF) model simulation. The WRF model produced more realistic geographical distributions of precipitation but cannot compensate for errors in the global model. WRF underpredicts precipitation for all three cases; the best simulation generates 92% of observed precipitation.

Unobserved spurious convective rainfall can be generated by the WRF model following the passage of a frontal weather system. A possible mechanism is suggested that links this to underpredicted frontal rainfall.

Corresponding author address: Dr. Stacey Dravitzki, 107 Station St., Aspendale VIC 3195, Australia. E-mail: stacey.dravitzki@csiro.au

Abstract

This paper investigates the predictability of heavy precipitation in the economically important Waikato River basin of New Zealand. A 2-yr archive of Global Forecast System (GFS) model data to +180 h for the period August 2005–August 2007 forms the basis of the study. GFS model predictions of precipitation are compared to surface measurements from 22 stations in and around the river basin. Categorical hit rate and bias, threat score, false-alarm ratio, probability of detection, RMSE, skill score, and mean error are all plotted as a function of model lead time for the 2-yr period.

The general synoptic structures of heavy precipitation events are often identified at long lead times. An example is shown for January 2006 when heavy rainfall is identified by GFS at +150 h, accurate location and timing is given at +48 h. Precipitation amounts are systematically underpredicted by GFS and the spatial distribution of rainfall is limited by model resolution. The value of GFS lies in its ability to provide early warning of potential heavy precipitation.

Three heavy rainfall events were selected for higher-resolution Weather Research and Forecasting (WRF) model simulation. The WRF model produced more realistic geographical distributions of precipitation but cannot compensate for errors in the global model. WRF underpredicts precipitation for all three cases; the best simulation generates 92% of observed precipitation.

Unobserved spurious convective rainfall can be generated by the WRF model following the passage of a frontal weather system. A possible mechanism is suggested that links this to underpredicted frontal rainfall.

Corresponding author address: Dr. Stacey Dravitzki, 107 Station St., Aspendale VIC 3195, Australia. E-mail: stacey.dravitzki@csiro.au
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