Multilocalization data assimilation for predicting heavy precipitation associated with a multiscale weather system

Abstract

High‐resolution numerical simulations are regularly used for severe weather forecasts. To improve model initial conditions, a single short localization is commonly applied in the ensemble Kalman filter when assimilating observations. This approach prevents large‐scale corrections from appearing in a high‐resolution analysis. To improve heavy rainfall forecasts associated with a multiscale weather system, analyses must be accurate across a range of spatial scales, a task that is difficult to accomplish using a single localization. This study is the first to apply a dual‐localization (DL) method to improve high‐resolution analyses used to forecast a real‐case heavy rainfall event associated with a Meiyu front on 16 June 2008 in Taiwan. A Meiyu front is a multiscale weather system characterized by storm‐scale convection, a mesoscale front, and large‐scale southwesterly monsoonal flow. The use of the DL method to produce the analyses was able to correct both the synoptic‐scale moisture flux transported by southwesterly monsoonal flow and the mesoscale low‐level convergence offshore of southwestern Taiwan. As a result, the forecasted amount, pattern, and temporal evolution of the heavy rainfall event were improved.

An Investigation of the Sensitivity of Predicting a Severe Rainfall Event in Northern Taiwan to the Upstream Condition with a WRF-based Radar Data Assimilation System

“…1), the predictability of the afternoon thunderstorm in northern Taiwan, with similar precipitation accumulation as the one in southwestern Taiwan, has not been investigated with regards to the impact of radar data assimilation. The event in the current study has been well simulated by Tu et al (2014) and Yang et al (2017), but both studies did not include the assimilation of radar data. Particularly, we examine the issue associated with quality control (QC) for radar data and how this can affect the prediction of an afternoon thunderstorm in northern Taiwan.…”

A Case Study on the Impact of Ensemble Data Assimilation with GNSS-Zenith Total Delay and Radar Data on Heavy Rainfall Prediction

“…transform Kalman filter (Hunt et al 2007) with the WRF Model (Skamarock and Klemp 2008). The WRF-LETKF system has two components, one of which assimilates conventional observations for the synoptic-tomesoscale weather systems characterized by several hundreds of kilometers (Yang et al 2014(Yang et al , 2017 and the other assimilates radar data for convective-scale weather systems characterized by a few tens of kilometers (Tsai et al 2014). In both components, R-localization and multiplicative covariance inflation are used.…”

“…In both components, R-localization and multiplicative covariance inflation are used. ZTD data assimilation is a new component for convectivescale data assimilation, and the operator uses the package developed for assimilating GNSS-RO data (Yang et al 2014), including calculating the geodetic height and refractivity index. In addition, in this study, when both the ZTD and radar data are assimilated, radar data assimilation is conducted sequentially after ZTD data assimilation.…”

“…Studies with assimilation of spaceborne GNSS radio occultation (RO) data demonstrate positive impacts on both global weather prediction (Anlauf et al 2011;Healy 2008). The impact of GNSS RO on precipitation prediction with regional models is also identified in previous studies (Huang et al 2005;Liu et al 2012;Yang et al 2014;Huang et al 2016). However, the RO observational density is not sufficient for adequate convective-scale data assimilation.…”

“…However, the number of humidity observations is relatively lower than that of wind or temperature observations. Among the observing systems that provide moisture information, such as satellites and radiosondes, global navigation satellite systems (GNSS) are becoming important for providing reliable moisture information (Bengtsson et al 2003;Ma et al 2011;Yang et al 2014). GNSSbased observations take advantage of the fact that the radio rays emitted by GNSS satellites are bent when passing through the atmosphere, causing signaling delays as the rays are received.…”