J18.2 Short-Range (0–6 h) PQPFs of Typhoons from Time-Lagged Multimodel Ensembles Using LAPS

Wednesday, 13 January 2016: 4:30 PM
Room 242 ( New Orleans Ernest N. Morial Convention Center)
Hui-Ling Chang, Central Weather Bureau, Taipei, Taiwan; and H. Yuan and P. L. Lin

The Local Analysis and Prediction System (LAPS) used in this study has three main components, including data ingestion, diabatic data assimilation and mesoscale model forecast. Because the data assimilation procedure of LAPS can provide the initial fields of mesoscale models with diabatic information, it can effectively mitigate the major problem of short-range quantitative precipitation forecasts (QPFs)—the “spinup” problem during the early stage of model integration. Therefore, more accurate precipitation forecasts can be expected during the early prediction stage (0–6 h). This forecast system was developed by the Central Weather Bureau (CWB) in partnership with the National Oceanic and Atmospheric Administration/Earth System Research Laboratory/Global Systems Division (NOAA/ESRL/GSD), for the purpose of improving the capability of short-range QPFs for severe weather systems.

In addition to the spinup problem, the predictability in severe weather prediction (e.g., typhoons) is very low. Therefore, it is also important to develop a reliable short-range ensemble prediction system (EPS). Since the LAPS can mitigate the spinup problem, this study applies the LAPS as a basic tool to develop the short-range (0-6h) probabilistic quantitative precipitation forecasts (PQPFs) of typhoons from time-lagged multimodel ensembles. By doing so, the critical uncertainties in prediction processes can be captured and conveyed to the users. The ultimate goal is to provide valuable precipitation forecasts for typhoons based on the LAPS EPS.

The forecast data used in this study are generated from the operational LAPS EPS, including a total of 148 cases of 0–6-h PQPFs based on all typhoon cases in Taiwan during 2008 and 2009. Considering the coverage of verification data, the radar-estimated rainfall data from the quantitative precipitation estimation (QPE) and Segregation Using Multiple Sensors (QPESUMS), a system developed by the CWB and Water Resources Agency (WRA) in Taiwan in cooperation with the National Severe Storms Laboratory (NSSL) in the United States, were used as observation data. The radar QPEs from the QPESUMS, covering the island of Taiwan and its nearby sea areas, were calibrated with rain gauge observations in Taiwan land areas, but were not calibrated over the sea areas.

The spread-skill relationship is a critical measure of the quality of ensemble forecasts. The ensemble spread and root-mean-square error (RMSE) of ensemble mean are highly correlated in the LAPS EPS with a correlation of 0.96, which indicates a good spread-skill relationship. That is, the ensemble spread can well represent the forecast uncertainties. The reliability diagrams display that the LAPS EPS displays wet biases at all thresholds and the bias grows with increasing threshold. In addition, the relative operating characteristic area (ROC area), a measure of potential usefulness, are all greater than 0.7 at different thresholds, which indicates skillful discrimination. Therefore, though the LAPS EPS is obviously wet-biased, the forecast biases can be corrected to improve the skill of PQPFs through a linear regression calibration procedure.

Sensitivity experiments for two important factors affecting calibration results are conducted, including (1) the experiments on different training samples, and (2) the experiments on the inconsistency of observation accuracy. The first experiment reveals that the calibration results are sensitive to the training samples. Calibration should be performed based on consistent forecast biases between training and validation samples. The second experiment indicates that the accuracy of observation is inconsistent in the sea and land areas, and samples are dominated by the ocean ones. Therefore, individual calibration for these two areas is needed to ensure better calibration results.

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