scholarly journals PENGARUH ASIMILASI MODEL CUACA WEATHER RESEARCH FORECAST (WRF) DENGAN DATA RADIASI SATELIT TERHADAP ESTIMASI CURAH HUJAN (Studi Kasus Stasiun Meteorologi Pattimura‒Ambon Tanggal 24-25 Juli 2013)

2019 ◽  
Vol 3 ◽  
pp. 118
Author(s):  
Habib Burrahman ◽  
Andreas Kurniawan Silitonga ◽  
Ilham Haris Batubara ◽  
Ahmad Fadlan
Keyword(s):  
Time Series ◽  
Numerical Models ◽  
Wrf Model ◽  
Weather Forecast ◽  
Skill Score ◽  
Experimental Models ◽  
Observation Data ◽  
Initial Condition ◽  
Rainfall Forecasting ◽  
Numerical Weather

<p class="AbstractEnglish"><strong>Abstract:</strong> Numerical weather predictions are currently being developed to address the need for high resolution rainfall forecasting. However, numerical weather forecasts in Indonesia are still problematic in terms of the accuracy of numerical models. Several previous studies have shown that modeling accuracy is strongly influenced by errors in the initial condition data. This study examines efforts from the research and development of the Weather Forecast and Forecast (WRF) model of preliminary data using a satellite beam assimilation procedure for forecasting rainfall in the Ambon region for two different case studies in 2018. Six experimental models are carried out by assimilation of sensors AMSU-A and MHS satellites use the WRFDA 3DVar system. This research was conducted by increasing the assimilation analysis on the initial data model, analyzing the model skills in the dichotomy of rainfall predictions, rainfall criteria, spatial rainfall, and time series of rainfall accumulation compared to BMKG rainfall observation data. The results showed that the DA AMSU-A and MHS experiments correctly modified the initial condition data of the model. Meanwhile, the results of dichotomous verification revealed that the DA observation experiment had the highest skill score forecast compared to other assimilation. but more experiments are needed in the northern Sumatra area to provide more significant results.</p><p class="KeywordsEngish"><strong>Abstrak:</strong> Prediksi cuaca numerik saat ini terus dikembangkan untuk mengatasi kebutuhan akan ramalan curah hujan resolusi tinggi. Namun, ramalan cuaca numerik di Indonesia masih bermasalah dalam hal akurasi model numerik. Beberapa penelitian sebelumnya menunjukkan bahwa akurasi pemodelan sangat dipengaruhi oleh kesalahan dalam data kondisi awal. Penelitian ini mengkaji upaya-upaya dari penelitian dan pengembangan model Prakiraan Cuaca dan Prakiraan (WRF) data awal menggunakan prosedur asimilasi pancaran satelit untuk prakiraan curah hujan di wilayah Ambon untuk dua studi kasus pada musim yang berbeda selama 2018. Enam model eksperimental dijalankan dengan asimilasi sensor satelit AMSU-A dan MHS menggunakan WRFDA sistem 3DVar. Penelitian ini dilakukan dengan analisis peningkatan asimilasi pada model data awal, analisis keterampilan model pada dikotomi prediksi curah hujan, kriteria curah hujan, curah hujan spasial, dan time series akumulasi hujan dibandingkan dengan data pengamatan curah hujan BMKG. Hasil penelitian menunjukkan bahwa eksperimen DA AMSU-A dan MHS memodifikasi data kondisi awal model dengan benar. Sementara itu, hasil verifikasi dikotomis mengungkapkan bahwa eksperimen DA observasi memiliki skor ketrampilan prakiraan tertinggi dibandingkan dengan asimilasi lainnya. namun diperlukan lagi percobaan di daerah Sumatra utara untuk memberikan hasil yang lebih signifikan.</p>

Effect of the Ushant SST front on regional weather forecasting in the event of anticyclonic events

2020 ◽  
Author(s):  
Francoise Orain ◽  
Marie-Noelle Bouin ◽  
Jean-Luc Redelsperger ◽  
Valérie Garnier
Keyword(s):  
Weather Forecasting ◽  
Numerical Models ◽  
Weather Forecast ◽  
Satellite Observation ◽  
Observation Data ◽  
Satellite Sst ◽  
Hydrostatic Model ◽  
Definition Of ◽  
The Impact ◽  
North And South

&lt;p&gt;The representation of the Ushant front in Meteo-France numerical models is not accurate. The aim of this study is to evaluate the impact of a better representation of this front derived from SST satellite observation data on the weather forecast in Brittany.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;The study consisted in finding and selecting cases from 2016 to 2018 where the Ushant front was present in satellite SST analysis (high spatial and temporal resolution ) with differences in weather pattern between North and South Brittany. Then compare this to the operational Arome model (Meteo France non hydrostatic model).&lt;/p&gt;&lt;p&gt;Situations of disagreement between the model and the observations were selected. Some weather forecast simulations with Mesonh model (very close to Arome) were performed on these cases with a better definition of the Ushant front. We present some results.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals PENGARUH ASIMILASI DATA PENGINDERAAN JAUH (RADAR DAN SATELIT) PADA PREDIKSI CUACA NUMERIK UNTUK ESTIMASI CURAH HUJAN (IMPACT OF REMOTE SENSING DATA ASSIMILATION (RADAR AND SATELLITE) ON NUMERICAL WEATHER PREDICTION FOR RAINFALL ESTIMATION)

2018 ◽  
Vol 14 (2) ◽  
Author(s):  
Jaka A. I. Paski
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Initial Conditions ◽  
Weather Research And Forecasting ◽  
Observation Data ◽  
Initial Condition ◽  
Global Forecast System ◽  
Forecast System ◽  
Numerical Weather ◽  
Weather Research

One of the main problems in numerical weather modeling was the inaccuracy of initial condition data (initial conditions). This study reinforced the influence of assimilation of remote sensing observation data on initial conditions for predictive numerical rainfall in BMKG radar area Tangerang (Province of Banten and DKI Jakarta) on January 24, 2016. The procedure applied to rainfall forecast was the Weather Research and Forecasting model (WRF) with a down-to-down multi-nesting technique from Global Forecast System (GFS) output, the model was assimilated to radar and satellite image observation data using WRF Data Assimilation (WRFDA) 3DVAR system. Data was used as preliminary data from surface observation data, EEC C-Band radar data, AMSU-A satellite sensor data and MHS sensors. The analysis was done qualitatively by looking at the measurement scale. Observation data was used to know rainfall data. The results of the study showed that producing rainfall predictions with different assimilation of data produced different predictions. In general, there were improvements in the rainfall predictions with assimilation of satellite data was showing the best results. Abstrak Salah satu masalah utama pada pemodelan cuaca numerik adalah ketidak-akuratan data kondisi awal (initial condition). Penelitian ini menguji pengaruh asimilasi data observasi penginderaan jauh pada kondisi awal untuk prediksi numerik curah hujan di wilayah cakupan radar cuaca BMKG Tangerang (Provinsi Banten dan DKI Jakarta) pada 24 Januari 2016. Prosedur yang diterapkan pada prakiraan curah hujan adalah model Weather Research and Forecasting (WRF) dengan teknik multi-nesting yang di-downscale dari keluaran Global Forecast System (GFS), model ini diasimilasikan dengan data hasil observasi citra radar dan satelit menggunakan WRF Data Assimilation (WRFDA) sistem 3DVAR. Data yang digunakan sebagai kondisi awal berasal dari data observasi permukaan, data C-Band radar EEC, data satelit sensor AMSU-A dan sensor MHS. Analisis dilakukan secara kualitatif dengan melihat nilai prediksi spasial distribusi hujan terhadap data observasi GSMaP serta metode bias curah hujan antara model dan observasi digunakan untuk mengevaluasi pengaruh data asimilasi untuk prediksi curah hujan. Hasil penelitian yang diperoleh menunjukkan prediksi curah hujan dengan asimilasi data yang berbeda menghasilkan prediksi yang juga berbeda. Secara umum, asimilasi data penginderaan jauh memberikan perbaikan hasil prediksi estimasi curah hujan di mana asimilasi menggunakan data satelit menunjukan hasil yang paling baik.

scholarly journals Influence of Parameterization of Soil Processes on Meteorological Forecasts of Vertical Profiles

2016 ◽  
Vol 23 (3) ◽  
pp. 493-503 ◽  
Author(s):  
Grzegorz Duniec ◽  
Andrzej Mazur
Keyword(s):  
Numerical Models ◽  
Water Flux ◽  
Weather Forecast ◽  
Bare Soil ◽  
Expected Improvement ◽  
Physical Processes ◽  
Meteorological Model ◽  
Numerical Weather ◽  
Numerical Weather Forecast

Abstract Soil and atmosphere boundary layer (ABL) interact with each other and influence physical processes in soil and atmosphere. Quality of numerical weather forecast depends on good mapping of complex soil process (microphysics processes in soil, fluid dynamics in porous media, soil dynamics, water cycle in soil and soil-plant-water relation, thermal processes in the soil etc.) in parameterization soil schemes. Current parameterizations of soil physical processes in TERRA_ML (multilayer soil module of the COSMO meteorological model) were prepared 30 years ago for numerical model with poor resolution. Nowadays operationally numerical models work with much better resolution. So, previous parameterization must have been improved or prepared from the beginning if it is expected improvement quality of numerical weather forecast. The influence of changing parameterization of water flux through the soil for “bare soil” case on vertical meteorological profiles is presented in this paper. This influence can be seen not only in weather forecasts, but also in any areas where the results of meteorological model(s) are used, like decision support systems in emergency situations or modeling of dispersion of air pollutants.

scholarly journals Estimation of Damage Induced by Single-Hole Rock Blasting: A Review on Analytical, Numerical, and Experimental Solutions

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 29
Author(s):  
Mahdi Shadabfar ◽  
Cagri Gokdemir ◽  
Mingliang Zhou ◽  
Hadi Kordestani ◽  
Edmond V. Muho
Keyword(s):  
Structural Reliability ◽  
Numerical Models ◽  
Experimental Models ◽  
Rock Blasting ◽  
Discussion Section ◽  
Reliability Models ◽  
Fine Grain ◽  
Experimental Approaches ◽  
Engineering Projects

This paper presents a review of the existing models for the estimation of explosion-induced crushed and cracked zones. The control of these zones is of utmost importance in the rock explosion design, since it aims at optimizing the fragmentation and, as a result, minimizing the fine grain production and recovery cycle. Moreover, this optimization can reduce the damage beyond the set border and align the excavation plan with the geometric design. The models are categorized into three groups based on the approach, i.e., analytical, numerical, and experimental approaches, and for each group, the relevant studies are classified and presented in a comprehensive manner. More specifically, in the analytical methods, the assumptions and results are described and discussed in order to provide a useful reference to judge the applicability of each model. Considering the numerical models, all commonly-used algorithms along with the simulation details and the influential parameters are reported and discussed. Finally, considering the experimental models, the emphasis is given here on presenting the most practical and widely employed laboratory models. The empirical equations derived from the models and their applications are examined in detail. In the Discussion section, the most common methods are selected and used to estimate the damage size of 13 case study problems. The results are then utilized to compare the accuracy and applicability of each selected method. Furthermore, the probabilistic analysis of the explosion-induced failure is reviewed using several structural reliability models. The selection, classification, and discussion of the models presented in this paper can be used as a reference in real engineering projects.

scholarly journals Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2174
Author(s):  
Lijian Shi ◽  
Sen Liu ◽  
Yingni Shi ◽  
Xue Ao ◽  
Bin Zou ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Ocean Circulation ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Atmospheric Effects ◽  
Observation Data ◽  
Long Time Series ◽  
Ice Concentration ◽  
Long Time

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

scholarly journals A global association between Covid-19 cases and airborne particulate matter at regional level

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Angelo Solimini ◽  
F. Filipponi ◽  
D. Alunni Fegatelli ◽  
B. Caputo ◽  
C. M. De Marco ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Negative Binomial ◽  
Numerical Models ◽  
Regional Level ◽  
Pollution Level ◽  
Observation Data ◽  
Cumulative Number ◽  
Mixed Effect

AbstractEvidences of an association between air pollution and Covid-19 infections are mixed and inconclusive. We conducted an ecological analysis at regional scale of long-term exposure to air-borne particle matter and spread of Covid-19 cases during the first wave of epidemics. Global air pollution and climate data were calculated from satellite earth observation data assimilated into numerical models at 10 km resolution. Main outcome was defined as the cumulative number of cases of Covid-19 in the 14 days following the date when > 10 cumulative cases were reported. Negative binomial mixed effect models were applied to estimate the associations between the outcome and long-term exposure to air pollution at the regional level (PM10, PM2.5), after adjusting for relevant regional and country level covariates and spatial correlation. In total we collected 237,749 Covid-19 cases from 730 regions, 63 countries and 5 continents at May 30, 2020. A 10 μg/m3 increase of pollution level was associated with 8.1% (95% CI 5.4%, 10.5%) and 11.5% (95% CI 7.8%, 14.9%) increases in the number of cases in a 14 days window, for PM2.5 and PM10 respectively. We found an association between Covid-19 cases and air pollution suggestive of a possible causal link among particulate matter levels and incidence of COVID-19.

scholarly journals Intercomparison of Spatial Forecast Verification Methods: Identifying Skillful Spatial Scales Using the Fractions Skill Score

2010 ◽  
Vol 25 (1) ◽  
pp. 343-354 ◽  
Author(s):  
Marion Mittermaier ◽  
Nigel Roberts
Keyword(s):  
Spatial Scales ◽  
Wrf Model ◽  
Skill Score ◽  
Area Ratio ◽  
Forecast Verification ◽  
Care Needs ◽  
Forecast Performance ◽  
Verification Methods ◽  
Upper Level ◽  
Formed Part

Abstract The fractions skill score (FSS) was one of the measures that formed part of the Intercomparison of Spatial Forecast Verification Methods project. The FSS was used to assess a common dataset that consisted of real and perturbed Weather Research and Forecasting (WRF) model precipitation forecasts, as well as geometric cases. These datasets are all based on the NCEP 240 grid, which translates to approximately 4-km resolution over the contiguous United States. The geometric cases showed that the FSS can provide a truthful assessment of displacement errors and forecast skill. In addition, the FSS can be used to determine the scale at which an acceptable level of skill is reached and this usage is perhaps more helpful than interpreting the actual FSS value. This spatial-scale approach is becoming more popular for monitoring operational forecast performance. The study also shows how the FSS responds to forecast bias. A more biased forecast always gives lower FSS values at large scales and usually at smaller scales. It is possible, however, for a more biased forecast to give a higher score at smaller scales, when additional rain overlaps the observed rain. However, given a sufficiently large sample of forecasts, a more biased forecast system will score lower. The use of percentile thresholds can remove the impacts of the bias. When the proportion of the domain that is “wet” (the wet-area ratio) is small, subtle differences introduced through near-threshold misses can lead to large changes in FSS magnitude in individual cases (primarily because the bias is changed). Reliable statistics for small wet-area ratios require a larger sample of forecasts. Care needs to be taken in the choice of verification domain. For high-resolution models, the domain should be large enough to encompass the length scale of the typical mesoscale forcing (e.g., upper-level troughs or squall lines). If the domain is too large, the wet-area ratios will always be small. If the domain is too small, fluctuations in the wet-area ratio can be large and larger spatial errors may be missed. The FSS is a good measure of the spatial accuracy of precipitation forecasts. Different methods are needed to determine other patterns of behavior.

Fusion of rain radar images and wind forecasts in adeep learning model applied to rain nowcasting

2021 ◽  
Author(s):  
Anastase Charantonis ◽  
Vincent Bouget ◽  
Dominique Béréziat ◽  
Julien Brajard ◽  
Arthur Filoche
Keyword(s):  
Deep Learning ◽  
Optical Flow ◽  
Weather Forecast ◽  
Forecast Model ◽  
Radar Data ◽  
Learning Model ◽  
Rainfall Forecasting ◽  
Radar Images ◽  
Deep Learning Model ◽  
Rain Radar

&lt;p&gt;Short or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management or flood risks monitoring. Existing data-driven approaches, especially deep learning models, have shown significant skill at this task, using only rainfall radar images as inputs. In order to determine whether using other meteorological parameters such as wind would improve forecasts, we trained a deep learning model on a fusion of rainfall radar images and wind velocity produced by a weather forecast model. The network was compared to a similar architecture trained only on radar data, to a basic persistence model and to an approach based on optical flow. Our network outperforms by 8% the F1-score calculated for the optical flow on moderate and higher rain events for forecasts at a horizon time of 30 minutes. Furthermore, it outperforms by 7% the same architecture trained using only rainfall radar images. Merging rain and wind data has also proven to stabilize the training process and enabled significant improvement especially on the difficult-to-predict high precipitation rainfalls. These results can also be found in Bouget, V., B&amp;#233;r&amp;#233;ziat, D., Brajard, J., Charantonis, A., &amp; Filoche, A. (2020). Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting. arXiv preprint arXiv:2012.05015&lt;/p&gt;

scholarly journals Nondissipative and Dissipative Momentum Deposition by Mountain Wave Events in Sheared Environments

2018 ◽  
Vol 75 (8) ◽  
pp. 2721-2740 ◽  
Author(s):  
Christopher G. Kruse ◽  
Ronald B. Smith
Keyword(s):  
Broad Spectrum ◽  
Numerical Models ◽  
Wrf Model ◽  
Vertical Wind Shear ◽  
Mountain Wave ◽  
Subsequent Evolution ◽  
Event Duration ◽  
Mountain Waves ◽  
Vertical Dispersion ◽  
Nonlinear Transient

AbstractMountain waves (MWs) are generated during episodic cross-barrier flow over broad-spectrum terrain. However, most MW drag parameterizations neglect transient, broad-spectrum dynamics. Here, the influences of these dynamics on both nondissipative and dissipative momentum deposition by MW events are quantified in a 2D, horizontally periodic idealized framework. The influences of the MW spectrum, vertical wind shear, and forcing duration are investigated. MW events are studied using three numerical models—the nonlinear, transient WRF Model; a linear, quasi-transient Fourier-ray model; and an optimally tuned Lindzen-type saturation parameterization—allowing quantification of total, nondissipative, and dissipative MW-induced decelerations, respectively. Additionally, a pseudomomentum diagnostic is used to estimate nondissipative decelerations within the WRF solutions. For broad-spectrum MWs, vertical dispersion controls spectrum evolution aloft. Short MWs propagate upward quickly and break first at the highest altitudes. Subsequently, the arrival of additional longer MWs allows breaking at lower altitudes because of their greater contribution to u variance. As a result, minimum breaking levels descend with time and event duration. In zero- and positive-shear environments, this descent is not smooth but proceeds downward in steps as a result of vertically recurring steepening levels. Nondissipative decelerations are nonnegligible and influence an MW’s approach to breaking, but breaking and dissipative decelerations quickly develop and dominate the subsequent evolution. Comparison of the three model solutions suggests that the conventional instant propagation and monochromatic parameterization assumptions lead to too much MW drag at too low an altitude.

Sign in / Sign up

Export Citation Format

Share Document