Predicting well-connected SEP events from observations of solar EUVs and energetic protons

This study shows a quantitative assessment of the use of Extreme Ultraviolet (EUV) observations in the prediction of Solar Energetic Proton (SEP) events. The UMASEP scheme (Space Weather, 9, S07003, 2011; 13, 2015, 807–819) forecasts the occurrence and the intensity of the first hours of SEP events. In order to predict well-connected events, this scheme correlates Solar Soft X-rays (SXR) with differential proton fluxes of the GOES satellites. In this study, we explore the use of the EUV time history from GOES-EUVS and SDO-AIA instruments in the UMASEP scheme. This study presents the results of the prediction of the occurrence of well-connected >10 MeV SEP events, for the period from May 2010 to December 2017, in terms of Probability of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), and the average and median of the warning times. The UMASEP/EUV-based models were calibrated using GOES and SDO data from May 2010 to October 2014, and validated using out-of-sample SDO data from November 2014 to December 2017. The best results were obtained by those models that used EUV data in the range 50–340 Å. We conclude that the UMASEP/EUV-based models yield similar or better POD results, and similar or worse FAR results, than those of the current real-time UMASEP/SXR-based model. The reason for the higher POD of the UMASEP/EUV-based models in the range 50–340 Å, was due to the high percentage of successful predictions of well-connected SEP events associated with <C4 flares and behind-the-limb flares, which amounted to 25% of all the well-connected events during the period May 2010 to December 2017. By using all the available data (2010–2017), this study also concluded that the simultaneous use of SXRs and EUVs in 94 Å in the UMASEP-10 tool for predicting all >10 MeV SEP events, improves the overall performance, obtaining a POD of 92.9% (39/42) compared with 81% (34/42) of the current tool, and a slightly worse FAR of 31.6% (18/57) compared with 29.2% (14/58) of the current tool.

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An Empirical Blowing Snow Forecast Technique for the Canadian Arctic and the Prairie Provinces

Weather and Forecasting ◽

10.1175/waf-833.1 ◽

2005 ◽

Vol 20 (1) ◽

pp. 51-62 ◽

Cited By ~ 10

Author(s):

David G. Baggaley ◽

John M. Hanesiak

Keyword(s):

Wind Speed ◽

Canadian Arctic ◽

High Impact ◽

Probability Of Detection ◽

The Arctic ◽

Blowing Snow ◽

Critical Success ◽

Critical Success Index ◽

Success Index ◽

Prairie Provinces

Abstract Blowing snow has a major impact on transportation and public safety. The goal of this study is to provide an operational technique for forecasting high-impact blowing snow on the Canadian arctic and the Prairie provinces using historical meteorological data. The focus is to provide some guidance as to the probability of reduced visibilities (e.g., less than 1 km) in blowing snow given a forecast wind speed and direction. The wind character associated with blowing snow was examined using a large database consisting of up to 40 yr of hourly observations at 15 locations in the Prairie provinces and at 17 locations in the arctic. Instances of blowing snow were divided into cases with and without concurrent falling snow. The latter group was subdivided by the time since the last snowfall in an attempt to account for aging processes of the snowpack. An empirical scheme was developed that could discriminate conditions that produce significantly reduced visibility in blowing snow using wind speed, air temperature, and time since last snowfall as predictors. This process was evaluated using actual hourly observations to compute the probability of detection, false alarm ratio, credibility, and critical success index. A critical success index as high as 66% was achieved. This technique can be used to give an objective first guess of the likelihood of high-impact blowing snow using common forecast parameters.

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Evaluation of the Storm Prediction Center’s Day 1 Convective Outlooks

Weather and Forecasting ◽

10.1175/waf-d-12-00061.1 ◽

2012 ◽

Vol 27 (6) ◽

pp. 1580-1585 ◽

Cited By ~ 13

Author(s):

Nathan M. Hitchens ◽

Harold E. Brooks

Keyword(s):

Probability Of Detection ◽

Grid Spacing ◽

Forecast Performance ◽

Critical Success ◽

Initial Effort ◽

Detection Frequency ◽

Storm Prediction ◽

Critical Success Index ◽

Success Index

Abstract The Storm Prediction Center has issued daily convective outlooks since the mid-1950s. This paper represents an initial effort to examine the quality of these forecasts. Convective outlooks are plotted on a latitude–longitude grid with 80-km grid spacing and evaluated using storm reports to calculate verification measures including the probability of detection, frequency of hits, and critical success index. Results show distinct improvements in forecast performance over the duration of the study period, some of which can be attributed to apparent changes in forecasting philosophies.

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Visualizing Multiple Measures of Forecast Quality

Weather and Forecasting ◽

10.1175/2008waf2222159.1 ◽

2009 ◽

Vol 24 (2) ◽

pp. 601-608 ◽

Cited By ~ 163

Author(s):

Paul J. Roebber

Keyword(s):

Heavy Precipitation ◽

Probability Of Detection ◽

Ensemble Forecasts ◽

Convective Initiation ◽

Multiple Measures ◽

Critical Success ◽

Performance Statistics ◽

Critical Success Index ◽

Success Index ◽

Forecast Quality

Abstract A method for visually representing multiple measures of dichotomous (yes–no) forecast quality (probability of detection, false alarm ratio, bias, and critical success index) in a single diagram is presented. Illustration of the method is provided using performance statistics from two previously published forecast verification studies (snowfall density and convective initiation) and a verification of several new forecast datasets: Storm Prediction Center forecasts of severe storms (nontornadic and tornadic), Hydrometeorological Prediction Center forecasts of heavy precipitation (greater than 12.5 mm in a 6-h period), National Weather Service Forecast Office terminal aviation forecasts (ceiling and visibility), and medium-range ensemble forecasts of 500-hPa height anomalies. The use of such verification metrics in concert with more detailed investigations to advance forecasting is briefly discussed.

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PEMODELAN ATMOSFER DENGAN WRF PADA KEJADIAN BANJIR JAKARTA 17 JANUARI 2013

Jurnal Sains & Teknologi Modifikasi Cuaca ◽

10.29122/jstmc.v14i1.2679 ◽

2013 ◽

Vol 14 (1) ◽

pp. 29

Author(s):

Ardhi Adhary Arbain ◽

Mahally Kudsy ◽

M. Djazim Syaifullah

Keyword(s):

False Alarm ◽

Extreme Precipitation ◽

Model Performance ◽

Probability Of Detection ◽

Ncep Reanalysis ◽

Cold Surge ◽

Qualitative And Quantitative ◽

Critical Success ◽

Critical Success Index ◽

Success Index

Intisari Simulasi WRF pada tanggal 16-17 Januari 2013 dilakukan untuk menguji performa model dalam mendeteksi fenomena seruak dingin dan hujan ekstrim yang merupakan pemicu utama bencana banjir Jakarta pada periode tersebut. Metode verifikasi kualitatif dan kuantitatif pada tiap grid secara dikotomi digunakan untuk membandingkan keluaran model dengan data observasi Global Satellite Mapping of Precipitation (GSMaP) dan NCEP Reanalysis. Performa model WRF dihitung berdasarkan nilai akurasi (ACC), Critical Success Index (CSI), Probability of Detection (POD) dan False Alarm Ratio (FAR) yang diperoleh dari hasil verifikasi numerik. Hasil pengujian menunjukkan bahwa WRF mampu melakukan deteksi waktu awal kejadian hujan ekstrim dengan tepat setelah 6-7 jam sejak inisiasi model dilakukan. Performa terbaik WRF teramati pada pukul 02-09 WIB (LT) dengan nilai CSI mencapai 0,32, POD 0,82 dan FAR 0,66. Hasil verifikasi secara kualitatif dan kuantitatif juga menunjukkan bahwa WRF dapat melakukan deteksi seruak dingin dan hujan ekstrim sebelum banjir terjadi, walaupun dengan ketepatan durasi waktu dan lokasi kejadian yang masih relatif rendah bila dibandingkan dengan data observasi. Abstract WRF simulation on January 16-17, 2013 has been conducted to evaluate the model performance in detecting cold surge and extreme precipitation phenomena which were the triggers of Jakarta flood event during the period. Qualitative and quantitative dichotomous grid-to-grid verification methods are utilized to compare the model output with Global Satellite Mapping of Precipitation (GSMaP) observation and NCEP Reanalysis dataset. WRF model performance is calculated based on the scores of accuracy (ACC), Critical Success Index (CSI), Probability of Detection (POD) and False Alarm Ration (FAR) which are generated from numerical verification. The results show that WRF could precisely detect the onset of extreme precipitation event in 6-7 hours after the model initiation.The best performance of the model is observed at 02-09 WIB (LT) with CSI score of 0.32, POD 0.82 and FAR 0.66. Despite the model inability to accurately predict the duration and location of cold surge and extreme precipitation, the qualitative and quantitative verification results also show that WRF could detect the phenomena just before the flood event occured.

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Assessment of IMERG-V06, TRMM-3B42V7, SM2RAIN-ASCAT, and PERSIANN-CDR Precipitation Products over the Hindu Kush Mountains of Pakistan, South Asia

Remote Sensing ◽

10.3390/rs12233871 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3871

Author(s):

Ali Hamza ◽

Muhammad Naveed Anjum ◽

Muhammad Jehanzeb Masud Cheema ◽

Xi Chen ◽

Arslan Afzal ◽

...

Keyword(s):

Probability Of Detection ◽

Critical Success ◽

Hindu Kush ◽

Overall Performance ◽

Spatio Temporal ◽

Critical Success Index ◽

Precipitation Events ◽

Better Than ◽

Daily Time

In this study, the performances of four satellite-based precipitation products (IMERG-V06 Final-Run, TRMM-3B42V7, SM2Rain-ASCAT, and PERSIANN-CDR) were assessed with reference to the measurements of in-situ gauges at daily, monthly, seasonal, and annual scales from 2010 to 2017, over the Hindu Kush Mountains of Pakistan. The products were evaluated over the entire domain and at point-to-pixel scales. Different evaluation indices (Correlation Coefficient (CC), Root Mean Square Error (RMSE), Bias, and relative Bias (rBias)) and categorical indices (False Alarm Ration (FAR), Critical Success Index (CSI), Success Ratio (SR), and Probability of Detection (POD)) were used to assess the performances of the products considered in this study. Our results indicated the following. (1) IMERG-V06 and PERSIANN capably tracked the spatio-temporal variation of precipitation over the studied region. (2) All satellite-based products were in better agreement with the reference data on the monthly scales than on daily time scales. (3) On seasonal scale, the precipitation detection skills of IMERG-V06 and PERSIANN-CDR were better than those of SM2Rain-ASCAT and TRMM-3B42V7. In all seasons, overall performance of IMERG-V06 and PERSIANN-CDR was better than TRMM-3B42V7 and SM2Rain-ASCAT. (4) However, all products were uncertain in detecting light and moderate precipitation events. Consequently, we recommend the use of IMERG-V06 and PERSIANN-CDR products for subsequent hydro-meteorological studies in the Hindu Kush range.

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Rainfall Threshold for Flash Flood Warning Based on Model Output of Soil Moisture: Case Study Wernersbach, Germany

Water ◽

10.3390/w13081061 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1061

Author(s):

Thanh Thi Luong ◽

Judith Pöschmann ◽

Rico Kronenberg ◽

Christian Bernhofer

Keyword(s):

Soil Moisture ◽

Flash Flood ◽

Rainfall Threshold ◽

Probability Of Detection ◽

Water Levels ◽

Critical Success ◽

Flood Warning ◽

Critical Discharge ◽

Critical Success Index

Convective rainfall can cause dangerous flash floods within less than six hours. Thus, simple approaches are required for issuing quick warnings. The flash flood guidance (FFG) approach pre-calculates rainfall levels (thresholds) potentially causing critical water levels for a specific catchment. Afterwards, only rainfall and soil moisture information are required to issue warnings. This study applied the principle of FFG to the Wernersbach Catchment (Germany) with excellent data coverage using the BROOK90 water budget model. The rainfall thresholds were determined for durations of 1 to 24 h, by running BROOK90 in “inverse” mode, identifying rainfall values for each duration that led to exceedance of critical discharge (fixed value). After calibrating the model based on its runoff, we ran it in hourly mode with four precipitation types and various levels of initial soil moisture for the period 1996–2010. The rainfall threshold curves showed a very high probability of detection (POD) of 91% for the 40 extracted flash flood events in the study period, however, the false alarm rate (FAR) of 56% and the critical success index (CSI) of 42% should be improved in further studies. The proposed adjusted FFG approach has the potential to provide reliable support in flash flood forecasting.

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Exploring the potential of microwave diagnostics in SEP forecasting: The occurrence of SEP events

Journal of Space Weather and Space Climate ◽

10.1051/swsc/2017011 ◽

2017 ◽

Vol 7 ◽

pp. A13 ◽

Cited By ~ 13

Author(s):

Pietro Zucca ◽

Marlon Núñez ◽

Karl-Ludwig Klein

Keyword(s):

Time Series ◽

Time Derivative ◽

Time History ◽

Probability Of Detection ◽

Space Environment ◽

X Rays ◽

Prediction Scheme ◽

Significant Addition ◽

Using Data ◽

Microwave Time

Solar energetic particles (SEPs), especially protons and heavy ions, may be a space-weather hazard when they impact spacecraft and the terrestrial atmosphere. Forecasting schemes have been developed, which use earlier signatures of particle acceleration to predict the arrival of solar protons and ions in the space environment of the Earth. The UMASEP (University of MAlaga Solar particle Event Predictor) scheme forecasts the occurrence and the importance of an SEP event based on combined observations of soft X-rays, their time derivative and protons above 10 MeV at geosynchronous orbit. We explore the possibility to replace the derivative of the soft X-ray time history with the microwave time history in the UMASEP scheme. To this end we construct a continuous time series of observations for a 13-month period from December 2011 to December 2012 at two microwave frequencies, 4.995 and 8.8 GHz, using data from the four Radio Solar Telescope Network (RSTN) patrol stations of the US Air Force, and feed this time series to the UMASEP prediction scheme. During the selected period the Geostationary Operational Environmental Satellites (GOES) detected nine SEP events related to activity in the western solar hemisphere. We show that the SEP forecasting using microwaves has the same probability of detection as the method using soft X-rays, but no false alarm in the considered period, and a slightly increased warning time. A detailed analysis of the missed events is presented. We conclude that microwave patrol observations improve SEP forecasting schemes that employ soft X-rays. High-quality microwave data available in real time appear as a significant addition to our ability to predict SEP occurrence.

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APPLICATION OF A SPATIAL VERIFICATION METHOD TO GFS PRECIPITATION FORECASTS

Brazilian Journal of Geophysics ◽

10.22564/rbgf.v32i3.519 ◽

2014 ◽

Vol 32 (3) ◽

pp. 561

Author(s):

Fabiani Denise Bender ◽

Rita Yuri Ynoue

Keyword(s):

Weather Forecast ◽

Sao Paulo ◽

Probability Of Detection ◽

Precipitation Forecast ◽

São Paulo ◽

Verification Method ◽

Critical Success ◽

Object Based ◽

Centroid Distance ◽

Critical Success Index

BSTRACT. This study aims to describe a spatial analysis of precipitation field with the MODE tool, which consists in comparing features converted from griddedforecast and observed precipitation values. This evaluation was performed daily from April 2010 to March 2011, for the 36-h GFS precipitation forecast started at00 UTC over the state of São Paulo and neighborhood. Besides traditional verification measures, such as accuracy (A), critical success index (CSI), bias (BIAS),probability of detection (POD), and false alarm ratio (FAR); new verification measures are proposed, such as area ratio (AR), centroid distance (CD) and 50th and 90thpercentiles ratio of intensity (PR50 and PR90). Better performance was attained during the rainy season. Part of the errors in the simulations was due to overestimationof the forecasted intensity and precipitation areas.Keywords: object-based verification, weather forecast, precipitation, MODE, São Paulo. RESUMO. Este estudo tem como objetivo descrever uma análise espacial do campo de precipitação com a ferramenta MODE, que consiste em converter valores deprecipitação de grade do campo previsto e observado em objetos, que posteriormente serão comparados entre si. A avaliação é realizada diariamente sobre o estadode São Paulo e vizinhança, para o período de abril de 2010 a março de 2011, para as simulações do modelo GFS iniciadas às 00 UTC, na integração de 36 horas. Além da verificação através de índices tradicionais, como probabilidade de acerto (PA), índice crítico de sucesso (ICS), viés (VIÉS), probabilidade de detecção (PD)e razão de falso alarme (RFA), novos índices de avaliação são propostos, como razão de área (RA), distância do centroide (DC) e razão dos percentis 50 e 90 deintensidade (RP50 e RP90). O melhor desempenho ocorreu para a estação chuvosa. Parte dos erros nas simulações foi devido à superestimativa da intensidade e da área de abrangência dos eventos de precipitação em relação ao observado.Palavras-chave: avaliação baseada em objetos, previsão do tempo, precipitação, MODE, São Paulo.

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Identification of Range Overlaid Echoes Using Polarimetric Radar Measurements Based on a Fuzzy Logic Approach

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-15-0042.1 ◽

2016 ◽

Vol 33 (1) ◽

pp. 61-80 ◽

Cited By ~ 2

Author(s):

S.-G. Park ◽

Ji-Hyeon Kim ◽

Jeong-Seok Ko ◽

Gyuwon Lee

Keyword(s):

Fuzzy Logic ◽

Probability Of Detection ◽

Range Ambiguity ◽

Critical Success ◽

Skill Scores ◽

Fuzzy Logic Approach ◽

Logic Approach ◽

Radar Measurements ◽

Differential Reflectivity ◽

Critical Success Index

AbstractThe Ministry of Land, Infrastructure and Transport (MOLIT) of South Korea operates two S-band dual-polarimetric radars, as of 2013, to manage water resources through quantitative rainfall estimations at the surface level. However, the radar measurements suffer from range ambiguity. In this study, an algorithm based on fuzzy logic is developed to identify range overlaid echoes using seven inputs: standard deviations of differential reflectivity SD(ZDR), differential propagation phase SD(ϕDP), correlation coefficient SD(ρHV) and spectrum width SD(συ), mean of ρHV and συ, and difference of ϕDP from the system offset ΔϕDP. An examination of the algorithm’s performance shows that these echoes can be well identified and that echoes strongly affected by second trip are highlighted by high probabilities, over 0.6; echoes weakly affected have probabilities from 0.4 to 0.6; and those with low probabilities, below 0.4, are assigned as echoes without range ambiguity. A quantitative analysis of a limited number of cases using the usual skill scores shows that when the probability of 0.4 is considered as a threshold for identifying the range overlaid echoes, they can be identified with a probability of detection of 90%, a false alarm rate of 6%, and a critical success index of 84%.

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Forecasting Tornadoes Using Convection-Permitting Ensembles

Weather and Forecasting ◽

10.1175/waf-d-15-0134.1 ◽

2016 ◽

Vol 31 (1) ◽

pp. 273-295 ◽

Cited By ~ 36

Author(s):

Burkely T. Gallo ◽

Adam J. Clark ◽

Scott R. Dembek

Keyword(s):

Operating Characteristic ◽

Wrf Model ◽

Environmental Parameters ◽

Environmental Information ◽

Severe Weather ◽

Weather Research And Forecasting ◽

Critical Success ◽

Weather Parameters ◽

Critical Success Index ◽

Success Index

Abstract Hourly maximum fields of simulated storm diagnostics from experimental versions of convection-permitting models (CPMs) provide valuable information regarding severe weather potential. While past studies have focused on predicting any type of severe weather, this study uses a CPM-based Weather Research and Forecasting (WRF) Model ensemble initialized daily at the National Severe Storms Laboratory (NSSL) to derive tornado probabilities using a combination of simulated storm diagnostics and environmental parameters. Daily probabilistic tornado forecasts are developed from the NSSL-WRF ensemble using updraft helicity (UH) as a tornado proxy. The UH fields are combined with simulated environmental fields such as lifted condensation level (LCL) height, most unstable and surface-based CAPE (MUCAPE and SBCAPE, respectively), and multifield severe weather parameters such as the significant tornado parameter (STP). Varying thresholds of 2–5-km updraft helicity were tested with differing values of σ in the Gaussian smoother that was used to derive forecast probabilities, as well as different environmental information, with the aim of maximizing both forecast skill and reliability. The addition of environmental information improved the reliability and the critical success index (CSI) while slightly degrading the area under the receiver operating characteristic (ROC) curve across all UH thresholds and σ values. The probabilities accurately reflected the location of tornado reports, and three case studies demonstrate value to forecasters. Based on initial tests, four sets of tornado probabilities were chosen for evaluation by participants in the 2015 National Oceanic and Atmospheric Administration’s Hazardous Weather Testbed Spring Forecasting Experiment from 4 May to 5 June 2015. Participants found the probabilities useful and noted an overforecasting tendency.

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