An Assimilation and Forecasting Experiment of the Nerima Heavy Rainfa11 with a Cloud-Resolving Nonhydrostatic 4-Dimensional Variational Data Assimilation System

Abstract

The Meteorological Research Institute of the Japan Meteorological Agency has developed a cloudresolving nonhydrostatic 4-dimensional variational assimilation system (NHM-4DVAR), based on the Japan Meteorological Agency Nonhydrostatic Model (JMA-NHM), in order to investigate the mechanism of heavy rainfall events induced by mesoscale convective systems (MCSs). A horizontal resolution of the NHM-4DVAR is set to 2 km to resolve MCSs, and the length of the assimilation window is 1-hour. The control variables of the NHM-4DVAR are horizontal wind, vertical wind, nonhydrostatic pressure, potential temperature, surface pressure and pseudo relative humidity. Perturbations to the dynamical processes, and the advection of water vapor are considered, but these to the other physical processes are not taken into account.The NHM-4DVAR is applied to the heavy rainfall event observed at Nerima, central part of Tokyo metropolitan area, on 21 July 1999. Doppler radar's radial wind data, Global Positioning System's precipitable water vapor data, and surface temperature and wind data are assimilated as high temporal and spatial resolution data. The Nerima heavy rainfall is well reproduced in the assimilation and subseCorresponding author: Takuya Kawabata, Meteorological Research Institute, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan. E-mail: tkawabat@mri-jma.go.jp 1 Present affiliation: Frontier Research Center for Global Change, Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan.( 2007, Meteorological Society of Japan quent forecast, with respect to time sequence of 10-minute rainfall amount. The formation mechanism of the Nerima heavy rainfall is clarified from this study. A surface convergence line of horizontal winds was made of a southerly sea breeze and north-easterly winds over the Kanto plain around Nerima. Since the rise of temperature over the northern part of the Kanto plain was suppressed, due to a shield of clouds against sunshine, the difference of temperature between the convergence line and its northern side became large. Consequently, the wind convergence was enhanced around Nerima. An air with high equivalent potential temperature was lifted over this enhanced convergence line to generate cumulonimbi that caused the Nerima heavy rainfall.

30-second-Update 100-m-Mesh Data Assimilation Experiments: A Sudden Local Rain Case in Kobe on 11 September 2014

“…Among others, much attention has been paid to skillful NWP for severe weather (e.g., Kain et al 2006, Hohenegger andSchär 2007a, b;Kawabata et al 2007;Roberts and Lean 2008). Recently, the ensemble Kalman filter (EnKF; Evensen 1994Evensen , 2003 has become a major method in data assimilation (DA), and has contributed to investigate convection-permitting regional NWP (e.g., Zhang et al 2007;Stensrud et al 2009Stensrud et al , 2013Clark et al 2010;Schwartz et al 2010;Baldauf et al 2011;Melhauser and Zhang 2012;Yussolf et al 2013, Kunii 2014a, Weng and Zhang 2016.…”

Predictive skill of a subset of models participating in D‐PHASE in the COPS region

“…Wulfmeyer et al, 2006;Kawabata et al, 2007), large deficiencies remain in quantitative precipitation forecasting (QPF) (e.g. Grams et al, 2006).…”

Recent Progress of the NHM-4DVAR towards a Super-High Resolution Data Assimilation

“…The full model of JMANHM (Saito et al 2006;2012), which includes three-ice cloud microphysics without cumulus convection parameterization, was adopted as the forward model. The first version (v1) of the NHM-4DVAR considered perturbations only to dry dynamics and advection of water vapor (Kawabata et al 2007). Moreover, the second version (v2; Kawabata et al 2011) was implemented an additional warm rain process in the adjoint model (ADM).…”

“…For more precise forecasts of thunderstorms, it is necessary to assimilate observations representing their environmental fields with both dynamical and thermodynamical information. Kawabata et al (2007) developed a cloud-resolving 4D-Var (NHM-4DVAR.v1), and expanded to assimilate observations of radar reflectivity, GPS slant total delay, and Doppler lidar (NHM-4DVAR.v2; Kawabata et al 2011;2013;. These studies showed that the NHM-4DVAR.v2 with these various observation operators has the ability to predict a thunderstorm with a scale approximately less than 50 km.…”