scholarly journals Block level weather forecast using direct model output from NWP models during monsoon season in India

MAUSAM ◽  
2021 ◽  
Vol 68 (1) ◽  
pp. 23-40
Author(s):  
ASHOK KUMAR ◽  
NABANSU CHATTOPADHAYAY ◽  
Y. V. RAMARAO ◽  
K. K. SINGH ◽  
V. R. DURAI ◽  
...  
Keyword(s):  
Monsoon Season ◽  
Wrf Model ◽  
Weather Forecast ◽  
Oceanic Islands ◽  
Ratio Score ◽  
Rainfall Forecast ◽  
Skill Scores ◽  
Direct Model ◽  
Block Level ◽  
Weather Parameters

The forecast for 655 districts and 6500 blocks had been prepared and implemented on 1st June, 2014. The procedure for getting forecast for the districts  and  blocks in India including altitude corrections is based upon regular (0.25 × 0.25) grid output from the T-574 Model and output from  9 km WRF model. A verification study for rainfall forecast at 0.25 × 0.25 degree grid for Indian Window (0-40° N and 60-100° N) is also conducted, which had indicated that skill of the rainfall forecast is good for all parts of the country except oceanic islands and high terrain regions and one can down scale to any level, down to the blocks, the skill scores will not differ much. A detailed verification study for the skill of the forecast at block level for all the eight weather parameters for which the forecast was issued is conducted. The skill of the rainfall forecast is obtained for categorical forecast and as well as for yes/no forecast. The skill scores for rainfall had indicated that highest value of Hanssen and Kuiper (HK) score is 0.44, Hanssen and Kuiper score for quantitative rainfall (HKQ) is 0.18, Ratio score for yes/no forecast is 90 percent and Hit rate (HR) is 0.83. The detailed verification study for the block level weather forecast for monsoon 2014 is presented in the paper and the skill found is good. The study indicates that model forecast has the potential to be used for the block level forecast after making the quick value additions for which hints are given in the conclusion part.  

scholarly journals Evaluation of Combined Satellite and Radar Data Assimilation with POD-4DEnVar Method on Rainfall Forecast

2020 ◽  
Vol 10 (16) ◽  
pp. 5493 ◽  
Author(s):  
Jingnan Wang ◽  
Lifeng Zhang ◽  
Jiping Guan ◽  
Mingyang Zhang
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Rainfall Forecast ◽  
Skill Scores ◽  
The Individual ◽  
The Impact ◽  
Radar Data Assimilation

Satellite and radar observations represent two fundamentally different remote sensing observation types, providing independent information for numerical weather prediction (NWP). Because the individual impact on improving forecast has previously been examined, combining these two resources of data potentially enhances the performance of weather forecast. In this study, satellite radiance, radar radial velocity and reflectivity are simultaneously assimilated with the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar). The impact is evaluated on continuous severe rainfall processes occurred from June to July in 2016 and 2017. Results show that combined assimilation of satellite and radar data with POD-4DEnVar has the potential to improve weather forecast. Averaged over 22 forecasts, RMSEs indicate that though the forecast results are sensitive to different variables, generally the improvement is found in different pressure levels with assimilation. The precipitation skill scores are generally increased when assimilation is carried out. A case study is also examined to figure out the contributions to forecast improvement. Better intensity and distribution of precipitation forecast is found in the accumulated rainfall evolution with POD-4DEnVar assimilation. These improvements are attributed to the local changes in moisture, temperature and wind field. In addition, with radar data assimilation, the initial rainwater and cloud water conditions are changed directly. Both experiments can simulate the strong hydrometeor in the precipitation area, but assimilation spins up faster, strengthening the initial intensity of the heavy rainfall. Generally, the combined assimilation of satellite and radar data results in better rainfall forecast than without data assimilation.

scholarly journals Impact Assessment of Block Level Agro Advisories for Saving Input Cost of Farmers under Old Gangetic Plains of West Bengal - A Case Study in Malda

2020 ◽  
pp. 86-96
Author(s):  
Debjyoti Majumder ◽  
Rakesh Roy ◽  
F. H. Rahman ◽  
B. C. Rudra
Keyword(s):  
West Bengal ◽  
Monsoon Season ◽  
India Meteorological Department ◽  
Weather Forecast ◽  
Economic Benefits ◽  
Weather Data ◽  
New Delhi ◽  
Medium Range Weather Forecast ◽  
Net Returns ◽  
Block Level

Biweekly block level Agromet bulletins were disseminated based on medium range weather forecast with an objective to assess the effectiveness and usefulness of Block level Agro Advisory Services (AAS) and quantify the economic benefits through adopting the micro scale agromet advisory in their day to day agricultural operations at Malda, West Bengal. Two farmers groups were considered for the study on the basis of adoption and non-adoption of the agro-met advisories. Crop situation of these farmers were compared with nearby fields having the same crops where forecast were not adopted among non AAS farmers. The entire cost incurred along with yield and net returns were calculated from sowing to marketing of goods. Similarly, the weather forecast and actual weather data received from India Meteorological Department, New Delhi were compared to verify the accuracy of rainfall forecast for the year 2019-20 at GKMS centre, Malda KVK, West Bengal. It was apparent that the value of ratio score was higher during winter (84%) than pre-monsoon (80%), post-monsoon (79%) and monsoon (74%). However, the value of threat score was also found maximum during pre-monsoon season (79%). Statistical analysis like correlation coefficient, RMSE values of wind direction were found too high in all the four seasons to accept any homogeneity in the predicted and observed values. Blockwise verification of rainfall over the year showed the range of accuracy forecast for rainfall in between 67–76%. This forecast directly had a significant role in profit generation among the AAS adaptive farmers whose additional profit enhancement for maize cultivation was between 12% and 19% only towards cost of irrigation as compared to non-adaptive farmers. The study also showcased that the AAS adaptive farmers had a better livelihood as compared to non-AAS adaptive farmers.

scholarly journals Evaluation of WRF Model Simulations of Tornadic and Nontornadic Outbreaks Occurring in the Spring and Fall

2010 ◽  
Vol 138 (11) ◽  
pp. 4098-4119 ◽  
Author(s):  
Chad M. Shafer ◽  
Andrew E. Mercer ◽  
Lance M. Leslie ◽  
Michael B. Richman ◽  
Charles A. Doswell
Keyword(s):  
Wrf Model ◽  
Severe Weather ◽  
Weather Research And Forecasting ◽  
Synoptic Scale ◽  
Sea Level Pressure ◽  
Significant Events ◽  
Skill Scores ◽  
Time Of Year ◽  
Tornado Outbreaks ◽  
Thermodynamic Instability

Abstract Recent studies, investigating the ability to use the Weather Research and Forecasting (WRF) model to distinguish tornado outbreaks from primarily nontornadic outbreaks when initialized with synoptic-scale data, have suggested that accurate discrimination of outbreak type is possible up to three days in advance of the outbreaks. However, these studies have focused on the most meteorologically significant events without regard to the season in which the outbreaks occurred. Because tornado outbreaks usually occur during the spring and fall seasons, whereas the primarily nontornadic outbreaks develop predominantly during the summer, the results of these studies may have been influenced by climatological conditions (e.g., reduced shear, in the mean, in the summer months), in addition to synoptic-scale processes. This study focuses on the impacts of choosing outbreaks of severe weather during the same time of year. Specifically, primarily nontornadic outbreaks that occurred during the summer have been replaced with outbreaks that do not occur in the summer. Subjective and objective analyses of the outbreak simulations indicate that the WRF’s capability of distinguishing outbreak type correctly is reduced when the seasonal constraints are included. However, accuracy scores exceeding 0.7 and skill scores exceeding 0.5 using 1-day simulation fields of individual meteorological parameters, show that precursor synoptic-scale processes play an important role in the occurrence or absence of tornadoes in severe weather outbreaks. Low-level storm-relative helicity parameters and synoptic parameters, such as geopotential heights and mean sea level pressure, appear to be most helpful in distinguishing outbreak type, whereas thermodynamic instability parameters are noticeably both less accurate and less skillful.

scholarly journals Mountain Waves Analysis in the Vicinity of the Madrid-Barajas Airport Using the WRF Model

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Javier Díaz-Fernández ◽  
Lara Quitián-Hernández ◽  
Pedro Bolgiani ◽  
Daniel Santos-Muñoz ◽  
Ángel García Gago ◽  
...  
Keyword(s):  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Mountain Range ◽  
Aircraft Icing ◽  
Microphysics Scheme ◽  
Mountain Waves ◽  
Skill Scores ◽  
Stable Conditions ◽  
Pbl Scheme ◽  
Wind Velocities

Turbulence and aircraft icing associated with mountain waves are weather phenomena potentially affecting aviation safety. In this paper, these weather phenomena are analysed in the vicinity of the Adolfo Suárez Madrid-Barajas Airport (Spain). Mountain waves are formed in this area due to the proximity of the Guadarrama mountain range. Twenty different weather research and forecasting (WRF) model configurations are evaluated in an initial analysis. This shows the incompetence of some experiments to capture the phenomenon. The two experiments showing the best results are used to simulate thirteen episodes with observed mountain waves. Simulated pseudosatellite images are validated using satellite observations, and an analysis is performed through several skill scores applied to brightness temperature. Few differences are found among the different skill scores. Nevertheless, the Thompson microphysics scheme combined with the Yonsei university PBL scheme shows the best results. The simulations produced by this scheme are used to evaluate the characteristic variables of the mountain wave episodes at windward and leeward and over the mountain. The results show that north-northwest wind directions, moderate wind velocities, and neutral or slightly stable conditions are the main features for the episodes evaluated. In addition, a case study is analysed to evidence the WRF ability to properly detect turbulence and icing associated with mountain waves, even when there is no visual evidence available.

H2020 SINOPTICA (Satellite-borne and IN-situ Observations to Predict The Initiation of Convection for ATM) project: initial results

2021 ◽  
Author(s):  
Antonio Parodi ◽  
Marco Temme ◽  
Olga Gluchshenko ◽  
Markus Kerschbaum ◽  
Nicola Surian ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Traffic Management ◽  
Wrf Model ◽  
Weather Forecast ◽  
Extreme Weather Events ◽  
Weather Stations ◽  
Occultation Data ◽  
Nwp Model ◽  
Initial Results

<p>The H2020 SINOPTICA Project (2020-2022) aims at exploiting the untapped potential of assimilating remote sensing (EO-derived and ground-based radar) as well as GNSS-derived datasets (including radio occultation data) and in-situ weather stations data. Those data will be used for very high-resolution, very short-range numerical weather forecasts to improve the prediction of extreme weather events to the benefit of Air Traffic Management (ATM) operations. This will be done by setting up a continuously updated database of remote sensing-derived, GNSS-derived and in-situ weather stations variables, in combination with an automated assimilation system to feed an NWP model. SINOPTICA weather forecast results will be integrated into ATM decision-support tools, visualizing weather information on the controller's display, and generating new 4D trajectories to avoid severe weather areas. This contribution presents the initial results of the assimilation of aforementioned observations into the WRF model, operated at cloud-resolving grid spacing, for two case studies: a hailstorm event occured on 11 May 2019 nearby Malpensa airport and a severe convection episode occurred near Punta Raisi airport (Palermo) on 15 July 2020.</p>

Weather Nowcasting Using Environmental Sensors Integrated to the Mobile

2012 ◽  
pp. 183-203 ◽  
Author(s):  
K.G. Srinivasa ◽  
Harsha R ◽  
Kumar N. Sunil ◽  
Arhatha B ◽  
S.C. Abhishek ◽  
...  
Keyword(s):  
New Media ◽  
Mobile Devices ◽  
Mobile Device ◽  
Focal Point ◽  
Low Cost ◽  
Weather Forecast ◽  
Environmental Sensors ◽  
Grid Networks ◽  
Weather Parameters ◽  
Integrated Or

With the vagaries of nature being unpredictable, it’s now more important to have access to weather forecast for short periods of time. Many businesses, including those in agriculture and the fishing industry, depend on an hourly update of the weather. The access to such weather nowcasting data has, until now, been through traditional media like the television, radio, et cetera, while new media of communication such as mobile devices, have been largely unexplored. The advancement in MEMS (Micro Electrical Mechanical System) technology has now brought forth various sensors that are miniaturized and can be integrated or embedded into various other systems used presently. Environmental sensors that measure weather parameters are miniaturized to fit the size of a mobile. The system aims to integrate these sensors along with a mobile device so as to provide the capability of data measurement to the vast population that use mobile devices and thus create regional grid networks. The system aims to use the mobile for updating weather parameters as well to be the focal point of communication of the weather nowcasting information. As a result, the mobile device would provide targeted distribution of the weather information, which is more advantageous than the traditional means of mass distribution of information; also, as mobile technology acts as a focal point of gathering weather related parameters, it provides a twofold advantage for setting up a low cost, region specific weather monitoring system.

Towards operational postprocessing of cloud cover at MeteoSwiss

2020 ◽  
Author(s):  
Stephan Hemri ◽  
Christoph Spirig ◽  
Jonas Bhend ◽  
Lionel Moret ◽  
Mark Liniger
Keyword(s):  
Cloud Cover ◽  
Prediction Models ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Forecast Errors ◽  
Observation Data ◽  
Model Output ◽  
Ensemble Forecasts ◽  
Direct Model ◽  
Ensemble Model Output Statistics

<p>Over the last decades ensemble approaches have become state-of-the-art for the quantification of weather forecast uncertainty. Despite ongoing improvements, ensemble forecasts issued by numerical weather prediction models (NWPs) still tend to be biased and underdispersed. Statistical postprocessing has proven to be an appropriate tool to correct biases and underdispersion, and hence to improve forecast skill. Here we focus on multi-model postprocessing of cloud cover forecasts in Switzerland. In order to issue postprocessed forecasts at any point in space, ensemble model output statistics (EMOS) models are trained and verified against EUMETSAT CM SAF satellite data with a spatial resolution of around 2 km over Switzerland. Training with a minimal record length of the past 45 days of forecast and observation data already produced an EMOS model improving direct model output (DMO). Training on a 3 years record of the corresponding season further improved the performance. We evaluate how well postprocessing corrects the most severe forecast errors, like missing fog and low level stratus in winter. For such conditions, postprocessing of cloud cover benefits strongly from incorporating additional predictors into the postprocessing suite. A quasi-operational prototype has been set up and was used to explore meteogram-like visualizations of probabilistic cloud cover forecasts.</p>

A review of selected parameterization schemes of WRF model over Poland area in short-term weather forecast

2020 ◽  
Author(s):  
Sebastian Kendzierski
Keyword(s):  
Spatial Resolution ◽  
Dynamical Downscaling ◽  
Wrf Model ◽  
Grid Cell ◽  
High Sensitivity ◽  
Weather Forecast ◽  
Absolute Error ◽  
Initial Time ◽  
Physical Parameters ◽  
Model Configuration

<p>The aim of the work is to present simulation results of Weather Research and Forecasting (WRF) Model for high-resolution dynamical downscaling done over selected part of Poland. The research carried out a few unique simulations for selected days of the year 2019. For each model run different configuration of physical parameters (parametrization of boundary layer) were used. Additionally, two model runs were tested using the same configuration for physical parameterizations, but with two different spatial resolution. Additionally the sensitivity of the model in terms of spatial resolution was analyzed. Model was configured using two nested domains with 9 km and 3 km grid cell resolutions. All WRF simulations was simulated using GFS gribs with its initial time of 00 UTC. The results were compared with meteorological observations from meteorological stations. Results show high sensitivity of the obtained dynamical downscaling geophysical fields to the selected model configuration. High verifiability of air temperature forecasts was obtained using YSU and MYNN3 BL schemes. Mean Absolute Error (MAE) for temperature prediction has lower values in the summer season. Studies show the most optimal model configuration for BL for Poland area.</p>

scholarly journals Simulation of the Summer Monsoon Rainfall over East Asia Using the NCEP GFS Cumulus Parameterization at Different Horizontal Resolutions

2014 ◽  
Vol 29 (5) ◽  
pp. 1143-1154 ◽  
Author(s):  
Kyo-Sun Sunny Lim ◽  
Song-You Hong ◽  
Jin-Ho Yoon ◽  
Jongil Han
Keyword(s):  
Summer Monsoon ◽  
Monsoon Rainfall ◽  
Asian Summer Monsoon ◽  
Monsoon Season ◽  
Wrf Model ◽  
Horizontal Resolution ◽  
Cumulus Parameterization ◽  
Grid Spacing ◽  
Simulated Precipitation ◽  
Horizontal Grid

Abstract The most recent version of the simplified Arakawa–Schubert (SAS) cumulus scheme in the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) (GFS SAS) is implemented in the Weather Research and Forecasting (WRF) Model with a modification of the triggering condition and the convective mass flux in order to make it dependent on the model’s horizontal grid spacing. The East Asian summer monsoon season of 2006 is selected in order to evaluate the performance of the modified GFS SAS scheme. In comparison to the original GFS SAS scheme, the modified GFS SAS scheme shows overall better agreement with the observations in terms of the simulated monsoon rainfall. The simulated precipitation from the original GFS SAS scheme is insensitive to the model’s horizontal grid spacing, which is counterintuitive because the portion of the resolved clouds in a grid box should increase as the model grid spacing decreases. This behavior of the original GFS SAS scheme is alleviated by the modified GFS SAS scheme. In addition, three different cumulus schemes (Grell and Freitas, Kain and Fritsch, and Betts–Miller–Janjić) are chosen to investigate the role of a horizontal resolution on the simulated monsoon rainfall. Although the forecast skill of the surface rainfall does not always improve as the spatial resolution increases, the improvement of the probability density function of the rain rate with the smaller grid spacing is robust regardless of the cumulus parameterization scheme.

scholarly journals Advances in the WRF model for convection-resolving forecasting

2006 ◽  
Vol 7 ◽  
pp. 25-29 ◽  
Author(s):  
J. B. Klemp
Keyword(s):  
Wrf Model ◽  
Forecast Accuracy ◽  
Weather Forecast ◽  
Cumulus Parameterization ◽  
Weather Research And Forecasting ◽  
Convective Systems ◽  
Convective Processes ◽  
Forecast Models ◽  
Good Potential ◽  
Mesh Spacing

Abstract. The Weather Research and Forecasting (WRF) Model has been designed to be an efficient and flexible simulation system for use across a broad range of weather-forecast and idealized-research applications. Of particular interest is the use of WRF in nonhydrostatic applications in which moist-convective processes are treated explicitly, thereby avoiding the ambiguities of cumulus parameterization. To evaluate the capabilities of WRF for convection-resolving applications, real-time forecasting experiments have been conducted with 4 km horizontal mesh spacing for both convective systems in the central U.S. and for hurricanes approaching landfall in the southeastern U.S. These forecasts demonstrate a good potential for improving the forecast accuracy of the timing and location of these systems, as well as providing more detailed information on their structure and evolution that is not available in current coarser resolution operational forecast models.

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