scholarly journals The Operational JMA Nonhydrostatic Mesoscale Model

2006 ◽  
Vol 134 (4) ◽  
pp. 1266-1298 ◽  
Author(s):  
Kazuo Saito ◽  
Tsukasa Fujita ◽  
Yoshinori Yamada ◽  
Jun-ichi Ishida ◽  
Yukihiro Kumagai ◽  
...  
Keyword(s):  
Mesoscale Model ◽  
Weather Prediction ◽  
Time Integration ◽  
Model Performance ◽  
Density Perturbation ◽  
Horizontal Resolution ◽  
Japan Meteorological Agency ◽  
Staggered Grid ◽  
Meteorological Research Institute ◽  
Order Part

Abstract An operational nonhydrostatic mesoscale model has been developed by the Numerical Prediction Division (NPD) of the Japan Meteorological Agency (JMA) in partnership with the Meteorological Research Institute (MRI). The model is based on the MRI/NPD unified nonhydrostatic model (MRI/NPD-NHM), while several modifications have been made for operational numerical weather prediction with a horizontal resolution of 10 km. A fourth-order advection scheme considering staggered grid configuration is implemented. The buoyancy term is directly evaluated from density perturbation. A time-splitting scheme for advection has been developed, where the low-order (second order) part of advection is modified in the latter half of the leapfrog time integration. Physical processes have also been revised, especially in the convective parameterization and PBL schemes. A turbulent kinetic energy (TKE) diagnostic scheme has been developed to overcome problems that arise to predict TKE. The model performance for mesoscale NWP has been verified by comparison with a former operational hydrostatic mesoscale model of JMA. It is found that the new nonhydrostatic mesoscale model outperforms the hydrostatic model in the prediction of synoptic fields and quantitative precipitation forecasts.

scholarly journals Verification of Forecast Weather Surface Variables over Vietnam Using the National Numerical Weather Prediction System

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Tien Du Duc ◽  
Lars Robert Hole ◽  
Duc Tran Anh ◽  
Cuong Hoang Duc ◽  
Thuy Nguyen Ba
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Model Performance ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Prediction System ◽  
Regional Models ◽  
Global Spectral Model ◽  
Numerical Weather ◽  
The Us

The national numerical weather prediction system of Vietnam is presented and evaluated. The system is based on three main models, namely, the Japanese Global Spectral Model, the US Global Forecast System, and the US Weather Research and Forecasting (WRF) model. The global forecast products have been received at 0.25- and 0.5-degree horizontal resolution, respectively, and the WRF model has been run locally with 16 km horizontal resolution at the National Center for Hydro-Meteorological Forecasting using lateral conditions from GSM and GFS. The model performance is evaluated by comparing model output against observations of precipitation, wind speed, and temperature at 168 weather stations, with daily data from 2010 to 2014. In general, the global models provide more accurate forecasts than the regional models, probably due to the low horizontal resolution in the regional model. Also, the model performance is poorer for stations with altitudes greater than 500 meters above sea level (masl). For tropical cyclone performance validations, the maximum wind surface forecast from global and regional models is also verified against the best track of Joint Typhoon Warning Center. Finally, the model forecast skill during a recent extreme rain event in northeast Vietnam is evaluated.

scholarly journals Impact of the assimilation of lightning data on the precipitation forecast at different forecast ranges

2017 ◽  
Vol 14 ◽  
pp. 187-194 ◽  
Author(s):  
Stefano Federico ◽  
Marco Petracca ◽  
Giulia Panegrossi ◽  
Claudio Transerici ◽  
Stefano Dietrich
Keyword(s):  
Data Assimilation ◽  
Mesoscale Model ◽  
Weather Prediction ◽  
Horizontal Resolution ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Time Range ◽  
False Alarms ◽  
Forecast Time ◽  
The Impact

Abstract. This study investigates the impact of the assimilation of total lightning data on the precipitation forecast of a numerical weather prediction (NWP) model. The impact of the lightning data assimilation, which uses water vapour substitution, is investigated at different forecast time ranges, namely 3, 6, 12, and 24 h, to determine how long and to what extent the assimilation affects the precipitation forecast of long lasting rainfall events (> 24 h). The methodology developed in a previous study is slightly modified here, and is applied to twenty case studies occurred over Italy by a mesoscale model run at convection-permitting horizontal resolution (4 km). The performance is quantified by dichotomous statistical scores computed using a dense raingauge network over Italy. Results show the important impact of the lightning assimilation on the precipitation forecast, especially for the 3 and 6 h forecast. The probability of detection (POD), for example, increases by 10 % for the 3 h forecast using the assimilation of lightning data compared to the simulation without lightning assimilation for all precipitation thresholds considered. The Equitable Threat Score (ETS) is also improved by the lightning assimilation, especially for thresholds below 40 mm day−1. Results show that the forecast time range is very important because the performance decreases steadily and substantially with the forecast time. The POD, for example, is improved by 1–2 % for the 24 h forecast using lightning data assimilation compared to 10 % of the 3 h forecast. The impact of the false alarms on the model performance is also evidenced by this study.

Global climate simulations at 2.8 km on GPU with the ICON model

2020 ◽  
Author(s):  
Xavier Lapillonne ◽  
William Sawyer ◽  
Philippe Marti ◽  
Valentin Clement ◽  
Remo Dietlicher ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Weather Prediction ◽  
Time Integration ◽  
Horizontal Resolution ◽  
Performance Comparison ◽  
Quasi Biennial Oscillation ◽  
Numerical Weather

<p>The ICON modelling framework is a unified numerical weather and climate model used for applications ranging from operational numerical weather prediction to low and high resolution climate projection. In view of further pushing the frontier of possible applications and to make use of the latest evolution in hardware technologies, parts of the model were recently adapted to run on heterogeneous GPU system. This initial GPU port focus on components required for high-resolution climate application, and allow considering multi-years simulations at 2.8 km on the Piz Daint heterogeneous supercomputer. These simulations are planned as part of the QUIBICC project “The Quasi-Biennial Oscillation (QBO) in a changing climate”, which propose to investigate effects of climate change on the dynamics of the QBO.</p><p>Because of the low compute intensity of atmospheric model the cost of data transfer between CPU and GPU at every step of the time integration would be prohibitive if only some components would be ported to the accelerator. We therefore present a full port strategy where all components required for the simulations are running on the GPU. For the dynamics, most of the physical parameterizations and infrastructure code the OpenACC compiler directives are used. For the soil parameterization, a Fortran based domain specific language (DSL) the CLAW-DSL has been considered. We discuss the challenges associated to port a large community code, about 1 million lines of code, as well as to run simulations on large-scale system at 2.8 km horizontal resolution in terms of run time and I/O constraints. We show performance comparison of the full model on CPU and GPU, achieving a speed up factor of approximately 5x, as well as scaling results on up to 2000 GPU nodes. Finally we discuss challenges and planned development regarding performance portability and high level DSL which will be used with the ICON model in the near future.</p>

Global NWP Modeling of urban environments at ~1km resolution

2021 ◽  
Author(s):  
Joe McNorton ◽  
Nicolas Bousserez ◽  
Gabriele Arduini ◽  
Anna Agusti-Panareda ◽  
Gianpaolo Balsamo ◽  
...  
Keyword(s):  
Land Surface ◽  
Urban Areas ◽  
Weather Prediction ◽  
Model Performance ◽  
Horizontal Resolution ◽  
Urban Environments ◽  
Trace Gas Emissions ◽  
Urban Representation ◽  
Forecasting System ◽  
Future Work

<p>Urban areas make up only a small fraction of the Earth’s surface; however, they are home to over 50% of the global population. Accurate numerical weather prediction (NWP) forecasts in these areas offer clear societal benefits; however, land-atmosphere interactions are significantly different between urban and non-urban environments. Forecasting urban weather requires higher model resolution than the size of the urban domain, which is often achievable by regional but not global NWP models. Here we present the preliminary implementation of an urban scheme within the land surface component of the global Integrated Forecasting System (IFS), at recently developed ~1km horizontal resolution. We evaluate the representation error of fluxes and NWP variables at coarser resolutions (~9 km and ~31 km), using the high resolution as truth. We evaluate the feasibility of the scheme and its urban representation at ~1km scales. Availability of urban mapping data limit the affordable complexity of the global scheme; however, using generalisations model performance is improved over urban sites, even adopting simple schemes, and the modelled Urban Heat Island effects show broad agreement with observations. Several directions for future work are explored including a more complex urban representation, restructuring of the urban tiling and the introduction of an urban emissions model for trace gas emissions.<strong> </strong></p>

Convection is key to better polar low forecasts

2020 ◽  
Author(s):  
Matilda Hallerstig ◽  
Linus Magnusson ◽  
Erik Kolstad
Keyword(s):  
Prediction Models ◽  
Deterministic Model ◽  
Rapid Development ◽  
Weather Prediction ◽  
Model Performance ◽  
Horizontal Resolution ◽  
Added Value ◽  
Grid Spacing ◽  
Polar Lows ◽  
Polar Low

<p>ECMWF HRES and Arome Arctic are the operational Numerical Weather Prediction models that forecasters in northern Norway use to predict Polar lows in the Nordic and Barents Seas. These type of lows are small, but intense mesoscale cyclones with strong, gusty winds and heavy snow showers. They cause hazards like icing, turbulence, high waves and avalanches that threaten offshore activity and coastal societies in the area. Due to their small size and rapid development, medium range global models with coarser resolutions such as ECMWF have not been able to represent them properly. This was only possible with short range high resolution regional models like Arome. When ECMWF introduced their new HRES deterministic model with 9 km grid spacing, the potential for more precise polar low forecasts increased. Here we use case studies and sensitivity tests to examine the ability of ECMWF HRES to represent polar lows. We also evaluate what added value the Arome Arctic model with 2.5 km grid spacing gives. For verification, we use coastal meteorological stations and scatterometer winds. We found that convection has a greater impact on model performance than horizontal resolution. We also see that Arome Arctic produces higher wind speeds than ECMWF HRES. To improve performance during polar lows for models with a horizontal grid spacing less than 10 km, it is therefore more important to improve the understanding and formulation of convective processes rather than simply increasing horizontal resolution.</p>

scholarly journals Combination of ground-based and space-based GNSS tomography (2021-2025)

2021 ◽  
Author(s):  
Witold Rohm ◽  
Paweł Hordyniec ◽  
Gregor Möller ◽  
Maciej Kryza ◽  
Estera Trzcina ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Low Cost ◽  
Weather Prediction ◽  
Adjoint Operator ◽  
Model Performance ◽  
Horizontal Resolution ◽  
Global Navigation Satellite Systems ◽  
Full Potential ◽  
Satellite Systems ◽  
Weather Models

<p>Global Navigation Satellite Systems (GNSS) sense the atmosphere remotely and provide low-cost, high-quality information about its state. Nowadays, radio occultation (RO) profiles from space platforms and tropospheric delays from ground-based stations are routinely assimilated in Numerical Weather Models  (NWM).</p><p>In spite of provision of valuable information for weather forecasting, both space- and ground-based data have significant limitations. The RO technique has low horizontal resolution and does not provide reliable profiles in the first 3-5km of the troposphere. Whereas, the station-specific integrated value of troposphere are sparse and pose a problem to NWM adjoint operator for correcting model fields at different heights. These deficiencies could be resolved by the GNSS tomography technique that utilizes an inverse Radon transform to derive the 3D refractivity distribution over certain troposphere space. The combination of space-based and ground-based observations in the tomographic model will enable us to increase the number of intersections of GNSS signals and improve the refractivity solution within individual model locations. </p><p>The aim of this research is to harness the full potential of Space 4.0 era, rapidly growing numbers of RO and GNSS satellite constellations as well as low-cost GNSS ground-based networks worldwide. We will not only use current infrastructure but also examine impact of future constellations on model performance. 3D model of refractivity from dense observations should be an excellent tool in weather prediction. Our previous research proves that the assimilation of the GNSS tomography outputs into the NWM improves relative humidity and the short-term weather forecasts. Therefore, the research goal of this project is to assess the benefit of integrated tomography model on the severe weather prediction and urban scale weather models.</p>

scholarly journals JRAero: the Japanese Reanalysis for Aerosol v1.0

2017 ◽  
Vol 10 (9) ◽  
pp. 3225-3253 ◽  
Author(s):  
Keiya Yumimoto ◽  
Taichu Y. Tanaka ◽  
Naga Oshima ◽  
Takashi Maki
Keyword(s):  
Data Assimilation ◽  
Transport Model ◽  
Horizontal Resolution ◽  
Japan Meteorological Agency ◽  
Observation Data ◽  
Aerosol Transport ◽  
Aerosol Model ◽  
Meteorological Research Institute ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. A global aerosol reanalysis product named the Japanese Reanalysis for Aerosol (JRAero) was constructed by the Meteorological Research Institute (MRI) of the Japan Meteorological Agency. The reanalysis employs a global aerosol transport model developed by MRI and a two-dimensional variational data assimilation method. It assimilates maps of aerosol optical depth (AOD) from MODIS onboard the Terra and Aqua satellites every 6 h and has a TL159 horizontal resolution (approximately 1.1°  ×  1.1°). This paper describes the aerosol transport model, the data assimilation system, the observation data, and the setup of the reanalysis and examines its quality with AOD observations. Comparisons with MODIS AODs that were used for the assimilation showed that the reanalysis showed much better agreement than the free run (without assimilation) of the aerosol model and improved under- and overestimation in the free run, thus confirming the accuracy of the data assimilation system. The reanalysis had a root mean square error (RMSE) of 0.05, a correlation coefficient (R) of 0.96, a mean fractional error (MFE) of 23.7 %, a mean fractional bias (MFB) of 2.8 %, and an index of agreement (IOA) of 0.98. The better agreement of the first guess, compared to the free run, indicates that aerosol fields obtained by the reanalysis can improve short-term forecasts. AOD fields from the reanalysis also agreed well with monthly averaged global AODs obtained by the Aerosol Robotic Network (AERONET) (RMSE  =  0.08, R = 0. 90, MFE  =  28.1 %, MFB  =  0.6 %, and IOA  =  0.93). Site-by-site comparison showed that the reanalysis was considerably better than the free run; RMSE was less than 0.10 at 86.4 % of the 181 AERONET sites, R was greater than 0.90 at 40.7 % of the sites, and IOA was greater than 0.90 at 43.4 % of the sites. However, the reanalysis tended to have a negative bias at urban sites (in particular, megacities in industrializing countries) and a positive bias at mountain sites, possibly because of insufficient anthropogenic emissions data, the coarse model resolution, and the difference in representativeness between satellite and ground-based observations.

scholarly journals Optical Flow for Verification

2010 ◽  
Vol 25 (5) ◽  
pp. 1479-1494 ◽  
Author(s):  
Caren Marzban ◽  
Scott Sandgathe
Keyword(s):  
Optical Flow ◽  
Mesoscale Model ◽  
Forecast Error ◽  
Weather Prediction ◽  
Model Performance ◽  
Simulated Data ◽  
General Idea ◽  
Forecast Errors ◽  
Spatial Fields ◽  
Nwp Model

Abstract Modern numerical weather prediction (NWP) models produce forecasts that are gridded spatial fields. Digital images can also be viewed as gridded spatial fields, and as such, techniques from image analysis can be employed to address the problem of verification of NWP forecasts. One technique for estimating how images change temporally is called optical flow, where it is assumed that temporal changes in images (e.g., in a video) can be represented as a fluid flowing in some manner. Multiple realizations of the general idea have already been employed in verification problems as well as in data assimilation. Here, a specific formulation of optical flow, called Lucas–Kanade, is reviewed and generalized as a tool for estimating three components of forecast error: intensity and two components of displacement, direction and distance. The method is illustrated first on simulated data, and then on a 418-day series of 24-h forecasts of sea level pressure from one member [the Global Forecast System (GFS)–fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)] of the University of Washington’s Mesoscale Ensemble system. The simulation study confirms (and quantifies) the expectation that the method correctly assesses forecast errors. The method is also applied to a real dataset consisting of 418 twenty-four-hour forecasts spanning 2 April 2008–2 November 2009, demonstrating its value for analyzing NWP model performance. Results reveal a significant intensity bias in the subtropics, especially in the southern California region. They also expose a systematic east-northeast or downstream bias of approximately 50 km over land, possibly due to the treatment of terrain in the coarse-resolution model.

scholarly journals Cluster Analysis for Object-Oriented Verification of Fields: A Variation

2008 ◽  
Vol 136 (3) ◽  
pp. 1013-1025 ◽  
Author(s):  
Caren Marzban ◽  
Scott Sandgathe
Keyword(s):  
Cluster Analysis ◽  
Spatial Data ◽  
Mesoscale Model ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Model Performance ◽  
Object Oriented ◽  
Previous Method ◽  
The Other ◽  
False Alarms

Abstract In a recent paper, a statistical method referred to as cluster analysis was employed to identify clusters in forecast and observed fields. Further criteria were also proposed for matching the identified clusters in one field with those in the other. As such, the proposed methodology was designed to perform an automated form of what has been called object-oriented verification. Herein, a variation of that methodology is proposed that effectively avoids (or simplifies) the criteria for matching the objects. The basic idea is to perform cluster analysis on the combined set of observations and forecasts, rather than on the individual fields separately. This method will be referred to as combinative cluster analysis (CCA). CCA naturally lends itself to the computation of false alarms, hits, and misses, and therefore, to the critical success index (CSI). A desirable feature of the previous method—the ability to assess performance on different spatial scales—is maintained. The method is demonstrated on reflectivity data and corresponding forecasts for three dates using three mesoscale numerical weather prediction model formulations—the NCEP/NWS Nonhydrostatic Mesoscale Model (NMM) at 4-km resolution (nmm4), the University of Oklahoma’s Center for Analysis and Prediction of Storms (CAPS) Weather Research and Forecasting Model (WRF) at 2-km resolution (arw2), and the NCAR WRF at 4-km resolution (arw4). In the small demonstration sample herein, model forecast quality is efficiently differentiated when performance is assessed in terms of the CSI. In this sample, arw2 appears to outperform the other two model formulations across all scales when the cluster analysis is performed in the space of spatial coordinates and reflectivity. However, when the analysis is performed only on spatial data (i.e., when only the spatial placement of the reflectivity is assessed), the difference is not significant. This result has been verified both visually and using a standard gridpoint verification, and seems to provide a reasonable assessment of model performance. This demonstration of CCA indicates promise in quickly evaluating mesoscale model performance while avoiding the subjectivity and labor intensiveness of human evaluation or the pitfalls of non-object-oriented automated verification.

scholarly journals Numerical Simulation of Extreme Temperature (Heat Wave) in Bangladesh Using WRF-ARW Model

2020 ◽  
pp. 44-54
Author(s):  
Md. Abdul Aziz ◽  
M. A. Samad ◽  
M. R. Hasan ◽  
M. N. U. Bhuiyan ◽  
M. A. K. Mallik
Keyword(s):  
Heat Wave ◽  
Mesoscale Model ◽  
Extreme Temperature ◽  
Model Performance ◽  
Horizontal Resolution ◽  
Weather Research And Forecasting ◽  
Sea Level Pressure ◽  
Heat Wave Event ◽  
Arw Model ◽  
Wave Event

Every year Bangladesh experiences different types of natural hazards and heat wave is one of them. In the present study, an advanced high-resolution Weather Research and Forecasting (WRF-ARW) numerical mesoscale model is used to simulate a severe heat wave event occurred during April over Bangladesh and eastern part of India. The model is integrated for 6 days starting from UTC of 19 April to UTC of 24 April 2016, on a single domain of 10 km horizontal resolution. For validation of the model performance, the model simulated results of temperature at 2 m height, relative humidity (RH), mean sea level pressure (MSLP) at UTC of 6 days are compared with the BMD observed data. And the results indicate that the model is able to simulate the occurrence of the heat wave event with 6 days over Bangladesh.

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