Download or read book Evaluating Precipitation Forecasts from the High Resolution Ensemble Kalman Filter HREnKF Over the Pacific Northwest written by Phillipa Cookson-Hills and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "The Pacific Northwest endures some of the heaviest precipitation on the planet. Therefore, producing accurate precipitation forecasts for this area is essential to warn different sectors of society of the timing and intensity of precipitation events. However, forecasting precipitation remains difficult in general, and in the Pacific Northwest in particular, owing to the uncertain atmospheric conditions and complex terrain. In addition, observational precipitation estimation is difficult due to sparseness of the rain gauge data and to the limited radar coverage in complex terrain. In an effort to improve forecast skill in this region, Environment and Climate Change Canada developed a regional High-Resolution Ensemble Kalman Filter (HREnKF) ensemble prediction system. The HREnKF has a 2.5km resolution and assimilates surface and upper air observations every hour. To determine the benefit of increasing the resolution, quantitative precipitation forecasts (QPF) from the HREnKF are compared to forecasts from the lower resolution (15km) REnKF system, which assimilates data every 6 hours. Furthermore, to separate the impact of increasing horizontal resolution from increased data assimilation, forecasts are also generated from a Down-Scaled (DS) ensemble system, which is downscaled from the REnKF using the same grid as the HREnKF but with no explicit data assimilation. To evaluate model skill, rain gauges and several other radar-based gridded observational products are used as the verification products. QPFs from all models are then compared to the verification products using a suite of deterministic and probabilistic evaluation methods. Finally, observational uncertainties are also addressed and considered in performing the verification. Both the traditional verification metrics and the spatial evaluation indicate a better performance of the high-resolution system compared to the REnKF. However, the effect of data assimilation at high temporal and spatial resolution is found to be negligible, as the HREnKF performs very similarly to the DS for all cases. Therefore, the computationally less expensive DS could be more appropriate than the HREnKF to forecast precipitation over the Pacific Northwest." --
Download or read book On the Use of the Ensemble Kalman Filter for Torrential Rainfall Forecasts written by Yasumitsu Maejima and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Torrential rainfall is a threat to modern human society. To prevent severe disasters by the torrential rains, it is an essential to accurate the numerical weather prediction. This article reports an effort to improve torrential rainfall forecasts by the Ensemble Kalman Filter based on the recent studies. Two series of numerical experiments are reported in this chapter. One is a dense surface observation data assimilation for a disastrous rainfall event caused by active rainband maintained for a long time. Although an experiment with a conventional observation data set represents the rainband, the significant dislocation and the underestimated precipitation amount are found. By contrast, dense surface data assimilation contributes to improve both the location and surface precipitation amount of the rainband. The other is the rapid-update high-resolution experiment with every 30-second Phased Array Weather Radar (PAWR) data for an isolated convective system associated with a local torrential rain. The representation of this event is completely missed without the PAWR data, whereas the active convection is well represented including fine three-dimensional structure by PAWR data assimilation. Throughout these studies, the data assimilation by Ensemble Kalman Filter has a large positive impact on the forecasts for torrential rainfall events.
