Evaluation of MM5 model resolution when applied to prediction of National Fire Danger Rating indexes

2006 ◽  
Vol 15 (2) ◽  
pp. 147 ◽  
Author(s):  
Jeanne L. Hoadley ◽  
Miriam L. Rorig ◽  
Larry Bradshaw ◽  
Sue A. Ferguson ◽  
Kenneth J. Westrick ◽  
...  
Keyword(s):  
Extreme Event ◽  
Mesoscale Model ◽  
Model Performance ◽  
Fire Danger ◽  
Western Montana ◽  
Fire Season ◽  
Mm5 Model ◽  
Model Predictions ◽  
Northern Idaho

Weather predictions from the MM5 mesoscale model were used to compute gridded predictions of National Fire Danger Rating System (NFDRS) indexes. The model output was applied to a case study of the 2000 fire season in Northern Idaho and Western Montana to simulate an extreme event. To determine the preferred resolution for automating NFDRS predictions, model performance was evaluated at 36, 12, and 4 km. For those indexes evaluated, the best results were consistently obtained for the 4-km domain, whereas the 36-km domain had the largest mean absolute errors. Although model predictions of fire danger indexes are consistently lower than observed, analysis of time series results indicates that the model does well in capturing trends and extreme changes in NFDRS indexes.

The Effect of Model Resolution in Predicting Meteorological Parameters Used in Fire Danger Rating

2004 ◽  
Vol 43 (10) ◽  
pp. 1333-1347 ◽  
Author(s):  
Jeanne L. Hoadley ◽  
Ken Westrick ◽  
Sue A. Ferguson ◽  
Scott L. Goodrick ◽  
Larry Bradshaw ◽  
...  
Keyword(s):  
Mesoscale Model ◽  
Model Performance ◽  
Fire Danger ◽  
Fire Season ◽  
State University ◽  
Model Resolution ◽  
Winter Storms ◽  
Northern Idaho ◽  
In Fire ◽  
Verification Techniques

Abstract Previous studies of model performance at varying resolutions have focused on winter storms or isolated convective events. Little attention has been given to the static high pressure situations that may lead to severe wildfire outbreaks. This study focuses on such an event so as to evaluate the value of increased model resolution for prediction of fire danger. The results are intended to lay the groundwork for using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) as input to the National Fire Danger Rating System to provide gridded predictions of fire danger indices. Predicted weather parameters were derived from MM5 and evaluated at three different resolutions (36, 12, and 4 km). Model output was compared with observations during the 2000 fire season in western Montana and northern Idaho to help to determine the model's skill in predicting fire danger. For application in fire danger rating, little significant improvement was found in skill with increased model resolution using standard forecast verification techniques. Diurnal bias of modeled temperature and relative humidity resulted in errors larger than the differences between resolutions. Significant timing and magnitude errors at all resolutions could jeopardize accurate prediction of fire danger.

scholarly journals Study of tropical cyclone "Fanoos" using MM5 model – a case study

2009 ◽  
Vol 9 (1) ◽  
pp. 43-51 ◽  
Author(s):  
S. Ramalingeswara Rao ◽  
K. Muni Krishna ◽  
O. S. R. U. Bhanu Kumar
Keyword(s):  
Tropical Cyclone ◽  
East Coast ◽  
Mesoscale Model ◽  
India Meteorological Department ◽  
Cumulus Parameterization ◽  
Cumulus Parameterization Scheme ◽  
Sea Level Pressure ◽  
Mm5 Model ◽  
Made In

Abstract. Tropical cyclones are one of the most intense weather hazards over east coast of India and create a lot of devastation through gale winds and torrential floods while they cross the coast. So an attempt is made in this study to simulate track and intensity of tropical cyclone "Fanoos", which is formed over the Bay of Bengal during 5–10 December 2005 by using mesoscale model MM5. The simulated results are compared with the observed results of India Meteorological Department (IMD); results show that the cumulus parameterization scheme, Kain-Fritsch (KF) is more accurately simulated both in track and intensity than the other Betts-Miller (BM) and Grell Schemes. The reason for better performance of KF-1 scheme may be due to inclusion of updrafts and downdrafts. The model could predict the minimum Central Sea Level Pressure (CSLP) as 983 hPa as compared to the IMD reports of 984 hPa and the wind speed is simulated at maximum 63 m/s compared to the IMD estimates of 65 m/s. Secondly "Fanoos" development from the lagrangian stand point in terms of vertical distribution of Potential Vorticity (PV) is also carried out around cyclone centre.

scholarly journals Simulation of a Heavy Rainfall Event on 14 September 2004 over Dhaka, Bangladesh Using MM5 Model

2011 ◽  
Vol 3 (2) ◽  
pp. 261-270 ◽  
Author(s):  
M. N. Ahasan ◽  
Dr. M. A. M. Chowdhury ◽  
D. A. Quadir
Keyword(s):  
Heavy Rainfall ◽  
Mesoscale Model ◽  
Model Performance ◽  
Heavy Rain ◽  
Horizontal Resolution ◽  
Rainfall Event ◽  
Heavy Rainfall Event ◽  
Cumulus Scheme ◽  
Mm5 Model ◽  
The Right

An attempt has been made to simulate a heavy rainfall event on 14 September 2004 over Dhaka, Bangladesh using the fifth-generation PSU/NCAR Mesoscale model (MM5). This was an extraordinary rainfall event and recorded 341 mm rainfall in 24-h which was the highest ever recorded. The MM5 model was run on triple-nested domains at 45, 15, 5 km horizontal resolutions using Anthes-Kuo cumulus  scheme. The model performance was evaluated by examining the different predicted parameters like mean sea level pressure, upper and lower level circulations, moisture, windshear, vorticity, convergence and rainfall. The model derived rainfall was compared with TRMM rainfall. The present results indicate that the MM5 model with the right combination of the nesting domain, horizontal resolution and cumulus scheme was able to simulate the heavy rainfall event, and associated dynamical and thermo-dynamical features reasonably well. The MM5 model suggested that the highly localized heavy rain over Dhaka was the result of an interaction of the monsoon land depression with southwest summer monsoon weather systems. The analysis shows that the depression almost remains stationary over southwest Bangladesh and zone of heavy rain was laid over Dhaka, and required moisture have been supplied from the Bay of Bengal.Keywords: Depression; Heavy rainfall; TRMM; MM5 model; High resolution.© 2011 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved.doi:10.3329/jsr.v3i2.6656                 J. Sci. Res. 3 (2), 261-270 (2011)

scholarly journals Optimizing the Environmental and Economic Sustainability of Remote Community Infrastructure

2020 ◽  
Vol 12 (6) ◽  
pp. 2208 ◽  
Author(s):  
Jamie E. Filer ◽  
Justin D. Delorit ◽  
Andrew J. Hoisington ◽  
Steven J. Schuldt
Keyword(s):  
Environmental Impacts ◽  
Sustainability Assessment ◽  
Model Performance ◽  
Assessment Model ◽  
Remote Communities ◽  
Remote Community ◽  
Rural Villages ◽  
Sustainable Technologies ◽  
Study Results

Remote communities such as rural villages, post-disaster housing camps, and military forward operating bases are often located in remote and hostile areas with limited or no access to established infrastructure grids. Operating these communities with conventional assets requires constant resupply, which yields a significant logistical burden, creates negative environmental impacts, and increases costs. For example, a 2000-member isolated village in northern Canada relying on diesel generators required 8.6 million USD of fuel per year and emitted 8500 tons of carbon dioxide. Remote community planners can mitigate these negative impacts by selecting sustainable technologies that minimize resource consumption and emissions. However, the alternatives often come at a higher procurement cost and mobilization requirement. To assist planners with this challenging task, this paper presents the development of a novel infrastructure sustainability assessment model capable of generating optimal tradeoffs between minimizing environmental impacts and minimizing life-cycle costs over the community’s anticipated lifespan. Model performance was evaluated using a case study of a hypothetical 500-person remote military base with 864 feasible infrastructure portfolios and 48 procedural portfolios. The case study results demonstrated the model’s novel capability to assist planners in identifying optimal combinations of infrastructure alternatives that minimize negative sustainability impacts, leading to remote communities that are more self-sufficient with reduced emissions and costs.

scholarly journals Evaluating the accuracy and uncertainty of atmospheric and wave model hindcasts during severe events using model ensembles

2021 ◽  
Author(s):  
Ali Abdolali ◽  
Andre van der Westhuysen ◽  
Zaizhong Ma ◽  
Avichal Mehra ◽  
Aron Roland ◽  
...  
Keyword(s):  
Extreme Event ◽  
Numerical Models ◽  
Model Performance ◽  
Ground Truth ◽  
Wave Model ◽  
Atmospheric Model ◽  
Stationary Source ◽  
Source Point ◽  
Spectral Wave Model ◽  
Model Ensembles

AbstractVarious uncertainties exist in a hindcast due to the inabilities of numerical models to resolve all the complicated atmosphere-sea interactions, and the lack of certain ground truth observations. Here, a comprehensive analysis of an atmospheric model performance in hindcast mode (Hurricane Weather and Research Forecasting model—HWRF) and its 40 ensembles during severe events is conducted, evaluating the model accuracy and uncertainty for hurricane track parameters, and wind speed collected along satellite altimeter tracks and at stationary source point observations. Subsequently, the downstream spectral wave model WAVEWATCH III is forced by two sets of wind field data, each includes 40 members. The first ones are randomly extracted from original HWRF simulations and the second ones are based on spread of best track parameters. The atmospheric model spread and wave model error along satellite altimeters tracks and at stationary source point observations are estimated. The study on Hurricane Irma reveals that wind and wave observations during this extreme event are within ensemble spreads. While both Models have wide spreads over areas with landmass, maximum uncertainty in the atmospheric model is at hurricane eye in contrast to the wave model.

scholarly journals Regression Models for Soil Water Storage Estimation Using the ESA CCI Satellite Soil Moisture Product: A Case Study in Northeast Portugal

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Tomás de Figueiredo ◽  
Ana Caroline Royer ◽  
Felícia Fonseca ◽  
Fabiana Costa de Araújo Schütz ◽  
Zulimar Hernández
Keyword(s):  
Soil Moisture ◽  
Soil Water ◽  
Regression Models ◽  
Meteorological Data ◽  
Water Storage ◽  
Model Performance ◽  
Soil Water Balance ◽  
Soil Water Storage ◽  
The Relationship

The European Space Agency Climate Change Initiative Soil Moisture (ESA CCI SM) product provides soil moisture estimates from radar satellite data with a daily temporal resolution. Despite validation exercises with ground data that have been performed since the product’s launch, SM has not yet been consistently related to soil water storage, which is a key step for its application for prediction purposes. This study aimed to analyse the relationship between soil water storage (S), which was obtained from soil water balance computations with ground meteorological data, and soil moisture, which was obtained from radar data, as affected by soil water storage capacity (Smax). As a case study, a 14-year monthly series of soil water storage, produced via soil water balance computations using ground meteorological data from northeast Portugal and Smax from 25 mm to 150 mm, were matched with the corresponding monthly averaged SM product. Linear (I) and logistic (II) regression models relating S with SM were compared. Model performance (r2 in the 0.8–0.9 range) varied non-monotonically with Smax, with it being the highest at an Smax of 50 mm. The logistic model (II) performed better than the linear model (I) in the lower range of Smax. Improvements in model performance obtained with segregation of the data series in two subsets, representing soil water recharge and depletion phases throughout the year, outlined the hysteresis in the relationship between S and SM.

scholarly journals Modelling Exposure by Spraying Activities—Status and Future Needs

2021 ◽  
Vol 18 (15) ◽  
pp. 7737
Author(s):  
Stefan Hahn ◽  
Jessica Meyer ◽  
Michael Roitzsch ◽  
Christiaan Delmaar ◽  
Wolfgang Koch ◽  
...  
Keyword(s):  
Current Knowledge ◽  
Model Performance ◽  
Relevant Information ◽  
Systematic Evaluation ◽  
Model Accuracy ◽  
Efficient Manner ◽  
User Perspective ◽  
Model Predictions ◽  
Spray Applications

Spray applications enable a uniform distribution of substances on surfaces in a highly efficient manner, and thus can be found at workplaces as well as in consumer environments. A systematic literature review on modelling exposure by spraying activities has been conducted and status and further needs have been discussed with experts at a symposium. This review summarizes the current knowledge about models and their level of conservatism and accuracy. We found that extraction of relevant information on model performance for spraying from published studies and interpretation of model accuracy proved to be challenging, as the studies often accounted for only a small part of potential spray applications. To achieve a better quality of exposure estimates in the future, more systematic evaluation of models is beneficial, taking into account a representative variety of spray equipment and application patterns. Model predictions could be improved by more accurate consideration of variation in spray equipment. Inter-model harmonization with regard to spray input parameters and appropriate grouping of spray exposure situations is recommended. From a user perspective, a platform or database with information on different spraying equipment and techniques and agreed standard parameters for specific spraying scenarios from different regulations may be useful.

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