scholarly journals Diurnal cycle of rainfall as predicted by WRF model : Verification using Model Evaluation Tools software

MAUSAM ◽  
2021 ◽  
Vol 66 (3) ◽  
pp. 433-444
Author(s):  
SOMA SENROY ◽  
SUBHENDU BRATASAHA ◽  
ANANDA KUMARDAS ◽  
S.K.ROY BHOWMIK ◽  
P.K. KUNDU
Keyword(s):  
Diurnal Cycle ◽  
Model Evaluation ◽  
Wrf Model ◽  
Model Verification ◽  
Evaluation Tools

scholarly journals The Prediction of Diatom Abundance by Comparison of Various Machine Learning Methods

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Yuna Shin ◽  
Heesuk Lee ◽  
Young-Joo Lee ◽  
Dae Keun Seo ◽  
Bomi Jeong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Variable Selection ◽  
Roc Curve ◽  
Model Evaluation ◽  
Selection Method ◽  
The Other ◽  
Evaluation Tool ◽  
Evaluation Tools ◽  
Variable Selection Method ◽  
Boruta Algorithm

This study adopts two approaches to analyze the occurrence of algae at Haman Weir for Nakdong River; one is the traditional statistical method, such as logistic regression, while the other is machine learning technique, such as kNN, ANN, RF, Bagging, Boosting, and SVM. In order to compare the performance of the models, this study measured the accuracy, specificity, sensitivity, and AUC, which are representative model evaluation tools. The ROC curve is created by plotting association of sensitivity and (1-specificity). The AUC that is area of ROC curve represents sensitivity and specificity. This measure has two competitive advantages compared to other evaluation tools. One is that it is scale-invariant. It means that purpose of AUC is how well the model predicts. The other is that the AUC is classification-threshold-invariant. It shows that the AUC is independent of threshold because it is plotted association of sensitivity and (1-specificity) obtained by threshold. We chose AUC as a final model evaluation tool with two advantages. Also, variable selection was conducted using the Boruta algorithm. In addition, we tried to distinguish the better model by comparing the model with the variable selection method and the model without the variable selection method. As a result of the analysis, Boruta algorithm as a variable selection method suggested PO4-P, DO, BOD, NH3-N, Susp, pH, TOC, Temp, TN, and TP as significant explanatory variables. A comparison was made between the model with and without these selected variables. Among the models without variable selection method, the accuracy of RF analysis was highest, and ANN analysis showed the highest AUC. In conclusion, ANN analysis using the variable selection method showed the best performance among the models with and without variable selection method.

scholarly journals On the Diurnal Cycle of GPS-Derived Precipitable Water Vapor over Sumatra

2019 ◽  
Vol 76 (11) ◽  
pp. 3529-3552
Author(s):  
Giuseppe Torri ◽  
David K. Adams ◽  
Huiqun Wang ◽  
Zhiming Kuang
Keyword(s):  
Water Vapor ◽  
Diurnal Cycle ◽  
Wrf Model ◽  
Precipitable Water ◽  
Active Phase ◽  
Precipitable Water Vapor ◽  
Tectonic Activity ◽  
Western Coast ◽  
The Impact ◽  
Spectrometer Data

Abstract Convective processes in the atmosphere over the Maritime Continent and their diurnal cycles have important repercussions for the circulations in the tropics and beyond. In this work, we present a new dataset of precipitable water vapor (PWV) obtained from the Sumatran GPS Array (SuGAr), a dense network of GPS stations principally for examining seismic and tectonic activity along the western coast of Sumatra and several offshore islands. The data provide an opportunity to examine the characteristics of convection over the area in greater detail than before. In particular, our results show that the diurnal cycle of PWV on Sumatra has a single late afternoon peak, while that offshore has both a midday and a nocturnal peak. The SuGAr data are in good agreement with GPS radio occultation data from the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission, as well as with imaging spectrometer data from the Ozone Measuring Instrument (OMI). A comparison between SuGAr and the NASA Water Vapor Project (NVAP), however, shows significant differences, most likely due to discrepancies in the temporal and spatial resolutions. To further understand the diurnal cycle contained in the SuGAr data, we explore the impact of the Madden–Julian oscillation (MJO) on the diurnal cycle with the aid of the Weather Research and Forecasting (WRF) Model. Results show that the daily mean and the amplitude of the diurnal cycle appear smaller during the suppressed phase relative to the developing/active MJO phase. Furthermore, the evening/nighttime peaks of PWV offshore appear later during the suppressed phase of the MJO compared to the active phase.

Gravity Waves and Other Mechanisms Modulating the Diurnal Precipitation over One of the Rainiest Spots on Earth: Observations and Simulations in 2016

2020 ◽  
Vol 148 (9) ◽  
pp. 3933-3950
Author(s):  
Johanna Yepes ◽  
John F. Mejía ◽  
Brian Mapes ◽  
Germán Poveda
Keyword(s):  
Diurnal Cycle ◽  
Wrf Model ◽  
Early Morning ◽  
Weather Research And Forecasting ◽  
Near Surface ◽  
Additional Hypothesis ◽  
Convective Systems ◽  
Earth Observations ◽  
Diurnal Rainfall ◽  
Static Energy

ABSTRACT The diurnal cycle of precipitation and thermodynamic profiles over western Colombia are examined in new GPM satellite rainfall products, first-ever research balloon launches during 2016 over both sea and land, and numerical simulations with the Weather Research and Forecasting (WRF) Model. This paper evaluates the Mapes et al. mechanism for midnight–early morning coastal convection that propagates offshore: reduction of inhibition in the crests of lower-tropospheric internal waves. Shipborne balloon launches confirm the evening development of such inhibition by a warm overhang in saturation moist static energy (SMSE) near 700–800 hPa. This feature relaxes overnight, consistent with the disinhibition hypothesis for early morning rains. Over the coastal plain, soundings also show late afternoon increases in near-surface MSE large enough to predominate over the overhang’s inhibition effect, driving a second peak in the rainfall diurnal cycle. Parameterized convection simulations fail to simulate the observed coastal rainfall. Still, during a November 2016 wet spell, a cloud-permitting one-way nested 4 km simulation performs better, simulating morning coastal rainfall. In that simulation, however, early morning cooling in the 700–800 hPa layer appears mainly as a standing signal resembling the local radiative effect rather than as a propagating wave. We consider the additional hypothesis that the offshore propagation of that morning convection could involve advection or wind shear effects on organized convective systems. Strong easterlies at mountaintop level were indeed simulated, but that is one of the model’s strongest biases, so the mechanisms of the model’s partial success in simulating diurnal rainfall remain ambiguous.

scholarly journals PREDIKTIBILITAS CURAH HUJAN DIURNAL DI PULAU JAWA MENGGUNAKAN MODEL WRF [PREDICTIBILITY OF DIURNAL RAINFALL OVER JAVA ISLAND USING WRF]

2017 ◽  
Vol 14 (2) ◽  
pp. 57
Author(s):  
NFN Suaydhi
Keyword(s):  
Diurnal Cycle ◽  
Early Warning ◽  
Confidence Level ◽  
Early Warning System ◽  
Wrf Model ◽  
Warning System ◽  
Madden Julian Oscillation ◽  
Diurnal Cycles ◽  
Diurnal Rainfall ◽  
One Year

Indonesian region often experiences hydrometeorological disasters such as floods and landslides. To mitigate the losses from such disasters, an early warning system is needed. PSTA LAPAN has developed an early warning system called SADEWA (Satellite Disaster Early Warning System). The performance of this early warning system needs to be evaluated in order to increase the confidence level. The evaluation of the WRF performance in producing the prediction was carried out by analyzing the diurnal cycles of rainfall over Java and its surroundings using the results of WRF predictions implemented in SADEWA and GSMaP data for one year period (Maret 2014 Februari 2015). The contrasting diurnal cycles between Java island and its surrounding seas could be well simulated by the WRF model, both the amount and the frequency of the rainfall. However, the phase of diurnal cycle from the WRF prediction leads that of the observation by two hours and the amplitude of the simulated diurnal cycle is higher than the observed. The results also show that the WRF predictions could not simulate the effects of MJO (Madden-Julian Oscillation) on the diurnal cycles of rainfall over Java.ABSTRAKWilayah Indonesia sering mengalami bencana hidrometeorologi seperti banjir dan tanah longsor. Untuk mengurangi kerugian yang diakibatkan oleh kejadian bencana meteorologi diperlukan suatu sistem peringatan dini. PSTA LAPAN telah mengembangkan sebuah sistem peringatan dini yang diberi nama SADEWA (Satellite Disaster Early Warning System). Kinerja sistem peringatan dini seperti ini perlu dievaluasi agar tingkat kepercayaannya meningkat. Evaluasi kinerja hasil prediksi ini dilakukan dengan menganalisis siklus diurnal curah hujan di pulau Jawa dan sekitarnya pada data hasil prediksi WRF yang digunakan dalam SADEWA dan data GSMaP selama satu tahun (Maret 2014 Februari 2015). Siklus diurnal curah hujan yang kontras antara pulau Jawa dengan lautan sekitarnya mampu disimulasikan dengan baik oleh model WRF, baik dari jumlah maupun frekuensi curah hujannya. Namun fasa diurnal dari hasil prediksi WRF mendahului fasa data pengamatan sekitar dua jam dan mempunyai amplitudo lebih besar. Hasil analisis juga menunjukkan hasil prediksi WRF belum mampu mensimulasikan pengaruh MJO (Madden-Julian Oscillation) pada siklus diurnal curah hujan di Jawa.

scholarly journals Towards improved and more routine Earth system model evaluation in CMIP

2016 ◽  
Vol 7 (4) ◽  
pp. 813-830 ◽  
Author(s):  
Veronika Eyring ◽  
Peter J. Gleckler ◽  
Christoph Heinze ◽  
Ronald J. Stouffer ◽  
Karl E. Taylor ◽  
...  
Keyword(s):  
Model Evaluation ◽  
Performance Metrics ◽  
Evaluation Framework ◽  
Systematic Evaluation ◽  
Earth System ◽  
Model Errors ◽  
Model Intercomparison ◽  
Evaluation Tools ◽  
Uncertainty Estimates ◽  
Wide Range

Abstract. The Coupled Model Intercomparison Project (CMIP) has successfully provided the climate community with a rich collection of simulation output from Earth system models (ESMs) that can be used to understand past climate changes and make projections and uncertainty estimates of the future. Confidence in ESMs can be gained because the models are based on physical principles and reproduce many important aspects of observed climate. More research is required to identify the processes that are most responsible for systematic biases and the magnitude and uncertainty of future projections so that more relevant performance tests can be developed. At the same time, there are many aspects of ESM evaluation that are well established and considered an essential part of systematic evaluation but have been implemented ad hoc with little community coordination. Given the diversity and complexity of ESM analysis, we argue that the CMIP community has reached a critical juncture at which many baseline aspects of model evaluation need to be performed much more efficiently and consistently. Here, we provide a perspective and viewpoint on how a more systematic, open, and rapid performance assessment of the large and diverse number of models that will participate in current and future phases of CMIP can be achieved, and announce our intention to implement such a system for CMIP6. Accomplishing this could also free up valuable resources as many scientists are frequently "re-inventing the wheel" by re-writing analysis routines for well-established analysis methods. A more systematic approach for the community would be to develop and apply evaluation tools that are based on the latest scientific knowledge and observational reference, are well suited for routine use, and provide a wide range of diagnostics and performance metrics that comprehensively characterize model behaviour as soon as the output is published to the Earth System Grid Federation (ESGF). The CMIP infrastructure enforces data standards and conventions for model output and documentation accessible via the ESGF, additionally publishing observations (obs4MIPs) and reanalyses (ana4MIPs) for model intercomparison projects using the same data structure and organization as the ESM output. This largely facilitates routine evaluation of the ESMs, but to be able to process the data automatically alongside the ESGF, the infrastructure needs to be extended with processing capabilities at the ESGF data nodes where the evaluation tools can be executed on a routine basis. Efforts are already underway to develop community-based evaluation tools, and we encourage experts to provide additional diagnostic codes that would enhance this capability for CMIP. At the same time, we encourage the community to contribute observations and reanalyses for model evaluation to the obs4MIPs and ana4MIPs archives. The intention is to produce through the ESGF a widely accepted quasi-operational evaluation framework for CMIP6 that would routinely execute a series of standardized evaluation tasks. Over time, as this capability matures, we expect to produce an increasingly systematic characterization of models which, compared with early phases of CMIP, will more quickly and openly identify the strengths and weaknesses of the simulations. This will also reveal whether long-standing model errors remain evident in newer models and will assist modelling groups in improving their models. This framework will be designed to readily incorporate updates, including new observations and additional diagnostics and metrics as they become available from the research community.

scholarly journals Towards improved and more routine Earth system model evaluation in CMIP

2016 ◽  
Author(s):  
Veronika Eyring ◽  
Peter J. Gleckler ◽  
Christoph Heinze ◽  
Ronald J. Stouffer ◽  
Karl E. Taylor ◽  
...  
Keyword(s):  
Model Evaluation ◽  
Performance Metrics ◽  
Evaluation Framework ◽  
Systematic Evaluation ◽  
Earth System ◽  
Model Errors ◽  
Model Intercomparison ◽  
Evaluation Tools ◽  
Uncertainty Estimates ◽  
Wide Range

Abstract. The Coupled Model Intercomparison Project (CMIP) has successfully provided the climate community with a rich collection of simulation output from Earth system models (ESMs) that can be used to understand past climate changes and make projections and uncertainty estimates of the future. Confidence in ESMs can be gained because the models are based on physical principles and reproduce many important aspects of observed climate. Scientifically more research is required to identify the processes that are most responsible for systematic biases and the magnitude and uncertainty of future projections so that more relevant performance tests can be developed. At the same time, there are many aspects of ESM evaluation that are well-established and considered an essential part of systematic evaluation but are currently implemented ad hoc with little community coordination. Given the diversity and complexity of ESM model analysis, we argue that the CMIP community has reached a critical juncture at which many baseline aspects of model evaluation need to be performed much more efficiently to enable a systematic, open and rapid performance assessment of the large and diverse number of models that will participate in current and future phases of CMIP. Accomplishing this could also free up valuable resources as many scientists are frequently "re-inventing the wheel" by re-writing analysis routines for well-established analysis methods. A more systematic approach for the community would be to develop evaluation tools that are well suited for routine use and provide a wide range of diagnostics and performance metrics that comprehensively characterize model behaviour as soon as the output is published to the Earth System Grid Federation (ESGF). The CMIP infrastructure enforces data standards and conventions for model output accessible via ESGF, additionally publishing observations (obs4MIPs) and reanalyses (ana4MIPs) for Model Intercomparison Projects using the same data structure and organization. This largely facilitates routine evaluation of the models, but to be able to process the data automatically alongside the ESGF, the infrastructure needs to be extended with processing capabilities at the ESGF data nodes where the evaluation tools can be executed on a routine basis. Efforts are already underway to develop community-based evaluation tools, and we encourage experts to provide additional diagnostic codes that would enhance this capability for CMIP. At the same time, we encourage the community to contribute observations for model evaluation to the obs4MIPs archive. The intention is to produce through ESGF a widely accepted quasi-operational evaluation framework for climate models that would routinely execute a series of standardized evaluation tasks. Over time, as the capability matures, we expect to produce an increasingly systematic characterization of models, which, compared with early phases of CMIP, will more quickly and openly identify the strengths and weaknesses of the simulations. This will also expose whether long-standing model errors remain evident in newer models and will assist modelling groups in improving their models. This framework will be designed to readily incorporate updates, including new observations and additional diagnostics and metrics as they become available from the research community.

scholarly journals Observing and Forecasting Vog Dispersion from Kīlauea Volcano, Hawaii

2015 ◽  
Vol 96 (10) ◽  
pp. 1667-1686 ◽  
Author(s):  
Steven Businger ◽  
Roy Huff ◽  
Andre Pattantyus ◽  
Keith Horton ◽  
A. Jeff Sutton ◽  
...  
Keyword(s):  
Respiratory Health ◽  
Wrf Model ◽  
Kilauea Volcano ◽  
General Aviation ◽  
Model Verification ◽  
Volcanic Emissions ◽  
Wind Fields ◽  
Volcanic Gas ◽  
Kīlauea Volcano ◽  
Negative Impacts

Abstract Emissions from Kīlauea volcano, known locally as “vog” for volcanic smog, pose significant environmental and health risks to the Hawaiian community. The Vog Measurement and Prediction (VMAP) project was conceived to help mitigate the negative impacts of Kīlauea’s emissions. To date, the VMAP project has achieved the following milestones: i) created a custom application of the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT, hereafter Vog model) to produce statewide forecasts of the concentration and dispersion of sulfur dioxide (SO2) and sulfate aerosol from Kīlauea volcano; ii) developed an ultraviolet (UV) spectrometer array to provide near-real-time volcanic gas emission rate measurements for use as input into the Vog model; iii) developed and deployed a stationary array of ambient SO2 and meteorological sensors to record the spatial characteristics of Kīlauea’s gas plume in high temporal and spatial resolution for model verification; and iv) developed web-based tools to facilitate the dissemination of observations and model forecasts to provide guidance for safety officials and the public, and to raise awareness of the potential hazards of volcanic emissions to respiratory health, agriculture, and general aviation. Wind fields and thermodynamic data from the Weather Research and Forecasting (WRF) Model provide input to the Vog model, with a statewide grid spacing of 3 km and a 1-km grid covering the island of Hawaii. Validation of the Vog model forecasts is accomplished with reference to data from Hawaii State Department of Health ground-based air quality monitors. VMAP results show that this approach can provide useful guidance for the people of Hawaii.

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