scholarly journals PERFORMA KONVERGENSI ANGIN PERMUKAAN DIURNAL MODEL REANALISIS ERA5 DI BENUA MARITIM INDONESIA

2020 ◽  
Vol 20 (2) ◽  
pp. 59-65
Author(s):  
Achmad Fahruddin Rais ◽  
Soenardi Soenardi ◽  
Zubaidi Fanani ◽  
Pebri Surgiansyah
Keyword(s):  
Finite Difference ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Wind ◽  
Limited Area ◽  
Surface Winds ◽  
Limited Area Model ◽  
Area Model ◽  
Weather Research

IntisariPada penelitian ini, penulis mengkaji uji performa kualitatif konvergensi angin permukaan model reanalisis ERA5 di BMI yang dibandingkan dengan hasil penelitian menggunakan limited area model (LAM) oleh Qian, Im dan Eltahir serta Alfahmi et al. Konvergensi angin permukaan dan anomali angin permukaan dihitung dengan menggunakan finite difference.  Hasil penelitian menunjukkan bahwa model reanalisis ERA5 mampu mensimulasikan konvergensi anomali angin permukaan dengan baik terhadap model regional climate model (RegCM) maupun The MIT regional climate model (MRCM) resolusi 27 km di Pulau Jawa dan sekitarnya serta BMI bagian barat dengan nilai konvergensi yang lebih tinggi. Sedangkan terhadap model weather research forecast (WRF) 9 km di BMI bagian timur, model reanalisis ERA5 juga dapat mensimulasikan konvergensi angin permukaan, tetapi dengan nilai yang lebih rendah. Selain itu, model reanalisis ERA5 mensimulasikan konvergensi angin permukaan lebih cepat 2 jam di BMI bagian barat dan timur dibandingkan MRCM27 dan WRF. AbstractIn this study, we discuss the qualitative performance testing of ERA5 surface wind convergence over the Indonesia maritime continent (BMI) compared with research based on limited area model (LAM) by Qian, Im, and Eltahir and also Alfahmi et al. Wind surface convergence and wind surface anomalies convergence is calculated using finite-difference. The results show that the ERA5 reanalysis model can simulate convergence of surface wind anomalies compared with both regional climate model (RegCM) and 27 km MIT regional climate model (MRCM) over Java and also western BMI with higher convergence values. While ERA5 reanalysis model can also simulate convergence of surface winds, but with lower values compared to 9 km weather research forecast (WRF) model over eastern BMI. Besides, the ERA5 reanalysis model simulates convergence of surface winds, which is 2 hours faster over western and eastern BMI compared to MRCM27 and WRF.

scholarly journals Application of Boyd’s Periodization and Relaxation Method in a Spectral Atmospheric Limited-Area Model. Part I: Implementation and Reproducibility Tests

2012 ◽  
Vol 140 (10) ◽  
pp. 3137-3148 ◽  
Author(s):  
Piet Termonia ◽  
Fabrice Voitus ◽  
Daan Degrauwe ◽  
Steven Caluwaerts ◽  
Rafiq Hamdi
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Regional Climate ◽  
Three Dimensional ◽  
Relaxation Method ◽  
Atmospheric Modeling ◽  
Limited Area ◽  
Limited Area Model ◽  
Numerical Weather ◽  
Area Model

Abstract This paper describes the implementation of a proposal of Boyd for the periodization and relaxation of the fields in a full three-dimensional spectral semi-implicit semi-Lagrangian limited-area model structure of an atmospheric modeling system called HARMONIE that is used for numerical weather prediction and regional climate studies. Some first feasibility tests in an operational numerical weather prediction context are presented. They show that, in terms of standard operational forecast scores, Boyd’s windowing-based method provides comparable performance as the old existing spline-based periodization procedure. However, the real improvements of this method should be expected in specific cases of strong dynamical forcings at the lateral boundaries. An extensive demonstration of the superiority of this windowing-based method is provided in an accompanying paper.

Quantification of the Lateral Boundary Forcing of a Regional Climate Model Using an Aging Tracer

2008 ◽  
Vol 136 (12) ◽  
pp. 4980-4996 ◽  
Author(s):  
Philippe Lucas-Picher ◽  
Daniel Caya ◽  
Sébastien Biner ◽  
René Laprise
Keyword(s):  
Regional Climate Model ◽  
Atmospheric Circulation ◽  
Climate Model ◽  
Regional Climate ◽  
Internal Variability ◽  
Limited Area ◽  
Lateral Boundary ◽  
Time Step ◽  
Lateral Boundary Conditions ◽  
Residency Time

Abstract The present work introduces a new and useful tool to quantify the lateral boundary forcing of a regional climate model (RCM). This tool, an aging tracer, computes the time the air parcels spend inside the limited-area domain of an RCM. The aging tracers are initialized to zero when the air parcels enter the domain and grow older during their migrations through the domain with each time step in the integration of the model. This technique was employed in a 10-member ensemble of 10-yr (1980–89) simulations with the Canadian RCM on a large domain covering North America. The residency time is treated and archived as the other simulated meteorological variables, therefore allowing computation of its climate diagnostics. These diagnostics show that the domain-averaged residency time is shorter in winter than in summer as a result of the faster winter atmospheric circulation. The residency time decreases with increasing height above the surface because of the faster atmospheric circulation at high levels dominated by the jet stream. Within the domain, the residency time increases from west to east according to the transportation of the aging tracer with the westerly general atmospheric circulation. A linear relation is found between the spatial distribution of the internal variability—computed with the variance between the ensemble members—and residency time. This relation indicates that the residency time can be used as a quantitative indicator to estimate the level of control exerted by the lateral boundary conditions on the RCM simulations.

scholarly journals Projection of Changes in Future Surface Wind around Japan Using a Non-hydrostatic Regional Climate Model

SOLA ◽  
2013 ◽  
Vol 9 (0) ◽  
pp. 23-26 ◽  
Author(s):  
Mizuki Hanafusa ◽  
Hidetaka Sasaki ◽  
Akihiko Murata ◽  
Kazuo Kurihara
Keyword(s):  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Wind

scholarly journals Evaluation of an Air Pressure–Based Proxy for Storm Activity

2011 ◽  
Vol 24 (10) ◽  
pp. 2612-2619 ◽  
Author(s):  
Oliver Krueger ◽  
Hans von Storch
Keyword(s):  
Wind Speed ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Wind ◽  
Ground Level ◽  
Geostrophic Wind ◽  
Air Pressure ◽  
Storm Activity ◽  
Wind Speeds

Abstract Yearly percentiles of geostrophic wind speeds serve as a widely used proxy for assessing past storm activity. Here, daily geostrophic wind speeds are derived from a geographical triangle of surface air pressure measurements and are used to build yearly frequency distributions. It is commonly believed, however unproven, that the variation of the statistics of strong geostrophic wind speeds describes the variation of statistics of ground-level wind speeds. This study evaluates this approach by examining the correlation between specific annual (seasonal) percentiles of geostrophic and of area-maximum surface wind speeds to determine whether the two distributions are linearly linked in general. The analyses rely on bootstrap and binomial hypothesis testing as well as on analysis of variance. Such investigations require long, homogeneous, and physically consistent data. Because such data are barely existent, regional climate model–generated wind and surface air pressure fields in a fine spatial and temporal resolution are used. The chosen regional climate model is the spectrally nudged and NCEP-driven regional model (REMO) that covers Europe and the North Atlantic. Required distributions are determined from diagnostic 10-m and geostrophic wind speed, which is calculated from model air pressure at sea level. Obtained results show that the variation of strong geostrophic wind speed statistics describes the variation of ground-level wind speed statistics. Annual and seasonal quantiles of geostrophic wind speed and ground-level wind speed are positively linearly related. The influence of low-pass filtering is also considered and found to decrease the quality of the linear link. Moreover, several factors are examined that affect the description of storminess through geostrophic wind speed statistics. Geostrophic wind from sea triangles reflects storm activity better than geostrophic wind from land triangles. Smaller triangles lead to a better description of storminess than bigger triangles.

scholarly journals Filling the matrix: an ANOVA-based method to emulate regional climate model simulations for equally-weighted properties of ensembles of opportunity

2021 ◽  
Author(s):  
Ole B. Christensen ◽  
Erik Kjellström
Keyword(s):  
Regional Climate Model ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Surface Wind ◽  
Climate Data ◽  
Large Ensembles ◽  
Climate Model Simulations

AbstractCollections of large ensembles of regional climate model (RCM) downscaled climate data for particular regions and scenarios can be organized in a usually incomplete matrix consisting of GCM (global climate model) x RCM combinations. When simple ensemble averages are calculated, each GCM will effectively be weighted by the number of times it has been downscaled. In order to facilitate more equal and less arbitrary weighting among downscaled GCM results, we present a method to emulate the missing combinations in such a matrix, enabling equal weighting among participating GCMs and hence among regional consequences of large-scale climate change simulated by each GCM. This method is based on a traditional Analysis of Variance (ANOVA) approach. The method is applied and studied for fields of seasonal average temperature, precipitation and surface wind and for the 10-year return value of daily precipitation and of 10-m wind speed for a completely filled matrix consisting of 5 GCMs and 4 RCMs. We quantify the skill of the two averaging methods for different numbers of missing simulations and show that ensembles where lacking members have been emulated by the ANOVA technique are better at representing the full ensemble than corresponding simple ensemble averages, particularly in cases where only a few model combinations are absent. The technique breaks down when the number of missing simulations reaches the sum of the numbers of GCMs and RCMs. Also, the method is only useful when inter-simulation variability is limited. This is the case for the average fields that have been studied, but not for the extremes. We have developed analytical expressions for the degree of improvement obtained with the present method, which quantify this conclusion.

scholarly journals Comparison of 10-m Wind Forecasts from a Regional Area Model and QuikSCAT Scatterometer Wind Observations over the Mediterranean Sea

2007 ◽  
Vol 135 (5) ◽  
pp. 1945-1960 ◽  
Author(s):  
Christophe Accadia ◽  
Stefano Zecchetto ◽  
Alfredo Lavagnini ◽  
Antonio Speranza
Keyword(s):  
Mediterranean Sea ◽  
Wind Direction ◽  
Forecast Error ◽  
Surface Wind ◽  
Mediterranean Area ◽  
Limited Area ◽  
Limited Area Model ◽  
Area Model ◽  
Energy Evaluation ◽  
The Mediterranean

Abstract Surface wind forecasts from a limited-area model [the Quadrics Bologna Limited-Area Model (QBOLAM)] covering the entire Mediterranean area at 0.1° grid spacing are verified against Quick Scatterometer (QuikSCAT) wind observations. Only forecasts within the first 24 h in coincidence with satellite overpasses are used. Two years of data, from 1 October 2000 to 31 October 2002, have been considered, allowing for an adequate statistical assessment under different wind conditions. This has been carried out by analyzing the fields of the mean wind vectors, wind speed bias, correlation, difference standard deviation, steadiness, gustiness, and mean wind direction difference, in order to investigate spatial variability. Statistics have been computed on a seasonal basis. A comparison of satellite and forecast winds with measurements from three buoys was also performed. Some critical areas of the Mediterranean Sea where wind forecast quality is lower than average have been identified. Such areas correspond to semienclosed basins surrounded by important orography and to small regions at the lee side of the main islands. In open-sea regions the model underestimates wind strength from about 0.5 m s−1 in spring and summer to 1.0 m s−1 in winter, as evidenced by the existing biases against scatterometer data. Also, a wind direction bias (scatterometer minus model) generally between 5° and 15° exists. A survey of the identified and likely sources of forecast error is performed, indicating that orography representation plays an important role. Numerical damping is identified as a likely factor reducing forecast wind strength. The need for a correction scheme is envisaged to provide more accurate forcing for numerical sea state forecasting models, wind energy evaluation, and latent and/or sensible heat exchanges.

scholarly journals Filling the Matrix: An ANOVA-Based Method to Emulate Regional Climate Model Simulations for Equally-Weighted Properties of Ensembles of Opportunity

2021 ◽  
Author(s):  
Ole Bøssing Christensen ◽  
Erik Kjellström
Keyword(s):  
Regional Climate Model ◽  
Large Scale ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Model ◽  
Regional Climate ◽  
Surface Wind ◽  
Climate Data ◽  
Large Ensembles ◽  
Climate Model Simulations

Abstract Collections of large ensembles of regional climate model (RCM) downscaled climate data for particular regions and scenarios can be organized in a usually incomplete matrix consisting of GCM (global climate model) x RCM combinations. When simple ensemble averages are calculated, each GCM will effectively be weighted by the number of times it has been downscaled. In order to facilitate more equal and less random weighting among downscaled GCM results, we present a method to emulate the missing combinations in such a matrix, enabling equal weighting among participating GCMs and hence among regional consequences of large-scale climate change simulated by each GCM. This method is based on a traditional Analysis of Variance (ANOVA) approach. The method is applied and studied for fields of seasonal average temperature, precipitation and surface wind and for the 10-year return value of daily precipitation and of 10-m wind speed for a completely filled matrix consisting of 5 GCMs and 4 RCMs. We quantify the skill of the two averaging methods for different numbers of missing simulations and show that ensembles where lacking members have been emulated by the ANOVA technique are better at representing the full ensemble than corresponding simple ensemble averages, particularly in cases where only a few model combinations are absent. The technique breaks down when the number of missing simulations reaches the sum of the numbers of GCMs and RCMs.

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