Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...

Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model [presentation]

Traditionally, the nowcasting of precipitation was conducted to a large extent by means of extrapolation of observations, especially of radar ref lectivity. In recent years, the blending of traditional extrapolation-based techniques with high-resolution numerical weather prediction (NWP) is gaining popularity in the nowcasting community. The increased need of NWP products in nowcasting applications poses great challenges to the NWP community because the nowcasting application of high-resolution NWP has higher requirements on the quality and content of the initial conditions compared to longer-range NWP. Considerable progress has been made in the use of NWP for nowcasting thanks to the increase in computational resources, advancement of high-resolution data assimilation techniques, and improvement of convective-permitting numerical modeling. This paper summarizes the recent progress and discusses some of the challenges for future advancement.

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Use of NWP for Nowcasting Convective Precipitation: Recent Progress and Challenges

The Weather Research and Forecasting (WRF) model–based variational data assimilation system (WRF-Var) has been extended from three- to four-dimensional variational data assimilation (WRF 4D-Var) to meet the increasing demand for improving initial model states in multiscale numerical simulations and forecasts. The initial goals of this development include operational applications and support to the research community. The formulation of WRF 4D-Var is described in this paper. WRF 4D-Var uses the WRF model as a constraint to impose a dynamic balance on the assimilation. It is shown to implicitly evolve the background error covariance and to produce the flow-dependent nature of the analysis increments. Preliminary results from real-data 4D-Var experiments in a quasi-operational setting are presented and the potential of WRF 4D-Var in research and operational applications are demonstrated. A wider distribution of the system to the research community will further develop its capabilities and to encoura...

https://opensky.ucar.edu/islandora/object/articles%3A17466/datastream/PDF/view

Four-Dimensional Variational Data Assimilation for WRF : Formulation and Preliminary Results

Convection-permitting models (CPMs) have provided weather forecasting centres with a step-change in capabilities for forecasting rainfall. They are now used operationally to forecast precipitation in many parts of the world, including the UK. CPMs are models in which the dynamics of atmospheric convection is treated with sufficient accuracy in order to make it viable to switch off convection parametrization. This review describes the current state-of-the-art in operational CPM-based numerical weather prediction (NWP), primarily within the UK, and the historical development of CPMs. The characteristics of CPM systems and forecasts are highlighted and placed in an international context to recognize similar trends and highlight some differences. It is shown that the realism of CPM-based forecasts can provide improved subjective guidance on convection, and, when measured on appropriate scales, can improve rainfall forecasting skill compared to coarser-resolution NWP. Data assimilation techniques used with operational CPMs are reviewed and given historical context. Examples of new types of observations that may increase the skill of forecasts from improved initial conditions are discussed. CPM-based nowcasting systems are shown to provide considerable improvements in short-range forecasts of rapidly developing, intense systems. As a result, these CPM-based systems provide a new forecasting capability. Finally, the development of CPMs has also required new techniques to verify forecasts and define their skill. These have revealed that the lack of predictability of the smallest scales involving convection means that ensemble techniques are required to represent forecast uncertainty, resulting in a new capability to provide objective forecast probabilities of local precipitation.

Convection-permitting models: a step-change in rainfall forecasting

This article reviews a range of leading methods to model the background error covariance matrix (the B-matrix) in modern variational data assimilation systems Owing partly to its very large rank, the B-matrix is impossible to use in an explicit fashion in an operational setting and so methods have been sought to model its important properties in a practical way Because the B-matrix is such an important component of a data assimilation system, a large effort has been made in recent years to improve its formulation Operational variational assimilation systems use a form of control variable transform to model B This transform relates variables that exist in the assimilation's ‘control space’ to variables in the forecast model's physical space The mathematical basis on which the control variable transform allows the B-matrix to be modelled is reviewed from first principles, and examples of existing transforms are brought together from the literature The method allows a large rank matrix to be represented by a relatively small number of parameters, and it is shown how information that is not provided explicitly is filled in Methods use dynamical properties of the atmosphere (eg balance relationships) and make assumptions about the way that background errors are spatially correlated (eg homogeneity and isotropy in the horizontal) It is also common to assume that the B-matrix is static The way that these, and other, assumptions are built into systems is shown



The article gives an example of how a current method performs An important part of this article is a discussion of some new ideas that have been proposed to improve the method Examples include how a more appropriate use of balance relations can be made, how errors in the moist variables can be treated and how assumptions of homogeneity/isotropy and the otherwise static property of the B-matrix can be relaxed Key developments in the application of dynamics, wavelets, recursive filters and flow-dependent methods are reviewed The article ends with a round up of the methods and a discussion of future challenges that the field will need to address Copyright © 2008 Royal Meteorological Society

A review of forecast error covariance statistics in atmospheric variational data assimilation. II: Modelling the forecast error covariance statistics

A new variational data assimilation system for a non-hydrostatic model is being developed at the Japan Meteorological Agency (JMA) for operational use. Known as the JMA non-hydrostatic model variational data assimilation (JNoVA) system, it mainly functions as a four-dimensional variational data assimilation system, although it has an option to be used as a three-dimensional system.

The set of control variables consists of initial conditions of unbalanced horizontal winds, large-scale components of potential temperature and surface pressure, unbalanced temperature and pseudo relative humidity. In the control variable transformation, hydrostatic balance and geostrophic balance are considered explicitly and the effect of the surface friction is also considered implicitly. When calculating the background-error covariances by the NMC method, a low-pass filter is introduced to remove noise in potential temperature and surface pressure that degrades the quality of the balanced winds. The cut-off wavelength of the low-pass filter is set to 300 km, which is the scale at which the model's kinetic energy spectrum transits to the shallower slope characterizing mesoscale motions.

An adjoint model of the JMA non-hydrostatic model has been developed from scratch by hand for this system. Although some of the physics are simplified, all physical processes except the radiation are considered.

A preliminary data assimilation experiment with the JNoVA has been done for a heavy rainfall event. The results show that the quantitative precipitation forecast (in terms of the intensity, timing and position of the event) from the analysis by the JNoVA is improved over the forecast from the analysis by a four-dimensional variational system that employs the JMA hydrostatic spectral model. Copyright © 2005 Royal Meteorological Society

A pre-operational variational data assimilation system for a non-hydrostatic model at the Japan Meteorological Agency: formulation and preliminary results

The Meteorological Research Institute of the Japan Meteorological Agency has developed a cloud-resolving 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 subsequent 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.

https://www.jstage.jst.go.jp/article/jmsj/85/3/85_3_255/_pdf

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

The regional model‐based Mesoscale Ensemble Prediction System, MEPS , at the Japan Meteorological Agency

The impact of optimizing the air̶sea exchange coefficients and the initial state is examined for a tropical cyclone (TC)with the aim of improving an operational mesoscale four-dimensional variational data assimilation system. In our optimization approach for TC Chaba that approached the Ryukyu Islands in October 2010, the drag coefficient values are adjusted so that they saturate at extreme conditions, while the first guess increases with increasing wind speed. In addition, the latent heat exchange coefficient values are adjusted so that they significantly increase under extreme conditions relative to the first guess. Consequently, the term of the cost function, which represents the discrepancy between the model results and the observational data, decreases by 4.1̶22.4% relative to the existing system after some spin-up cycles. The intensity and location of the TC are brought close to those of the corresponding best track produced at the Japan Meteorological Agency. Our optimization approach has the potential to improve the forecast skill.

Yuki Honda

Papers

A pre-operational variational data assimilation system for a non-hydrostatic model at the Japan Meteorological Agency: formulation and preliminary results

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

The regional model‐based Mesoscale Ensemble Prediction System, MEPS , at the Japan Meteorological Agency

Simultaneous Optimization of Air-Sea Exchange Coefficients and Initial Conditions near a Tropical Cyclone Using JNoVA

Reanalysis and Reforecast of Typhoon Vera (1959) Using a Mesoscale Four-Dimensional Variational Assimilation System