A Case Study of Assimilating Lightning-Proxy Relative Humidity with WRF-3DVAR

Abstract

Abstract:Lightning network data, considered as a useful supplement to radar observations, are a good indicator of severe convection, and has high temporal and spatial resolution. In Numerical Weather Prediction (NWP) models, lightning data are a new source of data to improve the forecasting of convective systems. In this case study, lightning data assimilation is conducted by converting lightning data to water vapor mixing ratio via a simple smooth continuous function, with input variables of total flash rate and simulated graupel mixing ratio at 9 km gridded resolution. Relative humidity converted from the retrieved water vapor mixing ratio is assimilated into the background field utilizing the three-dimensional variational (3DVAR) method in WRFDA (the Weather Research and Forecasting model Data Assimilation system). The benefits of assimilating lightning data are demonstrated in a series of experiments using data from a strong convection event that affected Beijing, Tianjin, Hebei and Shandong Province, on 31 July 2007. A nested domain with resolutions of 9 km and 3 km is implemented. For this case, assimilating lightning data shows some improvements in predictions of both reflectivity and neighboring precipitation, and in the temperature, dew-point temperature and relative humidity profile after seven hours.

Case Study of a Retrieval Method of 3D Proxy Reflectivity from FY-4A Lightning Data and Its Impact on the Assimilation and Forecasting for Severe Rainfall Storms

“…Although it has been proved that the assimilation of lightning data can improve the initialization of NWP, the difficulty still exists that lightning is not a modeled or prognostic variable in most existing NWP models. Hence, the aim of assimilating lightning data is to find a suitable observation operator to transfer model or prognostic variables to lightning data, or to convert lightning data into some other observations which can be assimilated in the existing data assimilation (DA) systems [16,17].…”

An Integrated Flood Risk Assessment Model for Cities Located in the Transitional Zone between Taihang Mountains and North China Plain: A Case Study in Shijiazhuang, Hebei, China

“…The distribution of precipitation in North China plain is relatively uneven during the year, mainly concentrated in late July to early August, with precipitation accounting for more than 50% of the whole year. During this period, the warm and humid airflow [24] from the southeast direction accumulates on the windward slope (east slope) of Taihang Mountain, which often leads to torrential rain [25,26]. In addition, the rivers developed by Taihang Mountain are mostly forked rivers, and the precipitation is easy to collect in a short time, causing serious flood disasters in the transitional zone between Taihang Mountains and North China plain [27,28].…”

Evaluation of Lightning Forecasting Based on One Lightning Parameterization Scheme and Two Diagnostic Methods

“…The cloud-to-ground flash rates were converted into proxy radar reflectivity basing on an empirical relationship in GSI (Girdpoint Statistics Interpolation) code, and then assimilated into the WRF model by physical initialization [28], WRF-GSI cloud analysis [29] and ensemble square root filter [30]. Total flash rates were converted into the proxy of relative humidity based on the relationship promoted by Fierro et al [26] and then assimilated via three-dimensional variational (3DVar) [31]. These methods properly improved the form, development and movement of convective systems and revised the forecast of maximum reflectivity and precipitation.…”