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

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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Assessment of PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0 Rainfall Products over a Semi-Arid Subtropical Climatic Region

Water ◽

10.3390/w14020147 ◽

2022 ◽

Vol 14 (2) ◽

pp. 147

Author(s):

Muhammad Naveed Anjum ◽

Muhammad Irfan ◽

Muhammad Waseem ◽

Megersa Kebede Leta ◽

Usama Muhammad Niazi ◽

...

Keyword(s):

Probability Of Detection ◽

Spatiotemporal Variability ◽

Annual Rainfall ◽

Critical Success ◽

Punjab Province ◽

Critical Success Index ◽

Semi Arid ◽

Better Than

This study compares the performance of four satellite-based rainfall products (SRPs) (PERSIANN-CCS, PERSIANN-CDR, SM2RAIN-ASCAT, and CHIRPS-2.0) in a semi-arid subtropical region. As a case study, Punjab Province of Pakistan was considered for this assessment. Using observations from in-situ meteorological stations, the uncertainty in daily, monthly, seasonal, and annual rainfall estimates of SRPs at pixel and regional scales during 2010–2018 were examined. Several evaluation indices (Correlation Coefficient (CC), Root Mean Square Error (RMSE), Bias, and relative Bias (rBias), as well as categorical indices (Probability of Detection (POD), Critical Success Index (CSI), and False Alarm Ration (FAR)) were used to assess the performance of the SRPs. The following findings were found: (1) CHIRPS-2.0 and SM2RAIN-ASCAT products were capable of tracking the spatiotemporal variability of observed rainfall, (2) all SRPs had higher overall performances in the northwestern parts of the province than the other parts, (3) all SRP estimates were in better agreement with ground-based monthly observations than daily records, and (4) on the seasonal scale, CHIRPS-2.0 and SM2RAIN-ASCAT were better than PERSIANN-CCS and PERSIANN. In all seasons, CHIRPS-2.0 and SM2RAIN-ASCAT outperformed PERSIANN-CCS and PERSIANN-CDR. Based on our findings, we recommend that hydrometeorological investigations in Pakistan’s Punjab Province employ monthly estimates of CHIRPS-2.0 and SM2RAIN-ASCAT products.

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Spatial Pattern of Precipitation in GPM-Era Satellite Products against Rain Gauge Measurements over Nepal

Jalawaayu ◽

10.3126/jalawaayu.v1i2.41009 ◽

2021 ◽

Vol 1 (2) ◽

pp. 39-56

Author(s):

Bharat Badayar Joshi ◽

Munawar Ali ◽

Dibit Aryal ◽

Laxman Paneru ◽

Bhaskar Shrestha

Keyword(s):

High Elevation ◽

Rain Gauge ◽

Probability Of Detection ◽

Precipitation Pattern ◽

Critical Success ◽

Precipitation Estimates ◽

Critical Success Index ◽

Precipitation Events ◽

Selection Of ◽

Better Than

Precipitation in a mountainous region is highly variable due to the complex terrain. Satellite-based precipitation estimates are potential alternatives to gauge measurements in these regions, as these typical measurements are not available or are scarce in high elevation areas. However, the accuracy of these gridded precipitation datasets need to be addressed before further usage. In this study, an evaluation of the spatial precipitation pattern in satellite-based precipitation products is provided, including satellite-only (Integrated Multi satellite Retrievals for GPM IMERG-UCORR and Global Satellite Mapping of Precipitation (GSMaP-MVK) and gauge calibrated (IMERG-CORR and GSMaP-Gauge) products, with a spatial resolution of 0.1°, which is compared to 387-gauge measurements in Nepal from April 2014 to December 2016. The major results are as follows: (1) The gauge calibrated version 5 IMERG-CORR and version 6 GSMaP-Gauge are relatively better than the satellite-only datasets, although they all underestimate the observed precipitation. (2) The daily gauge calibrated GSMaP-Gauge performs fairly well in low and mid-elevation areas, whereas the monthly gauge calibrated IMERG-C performs better in high-elevation areas. (3) For the daily time scale, IMERG-CORR shows a better ability to detect the true precipitation (higher Probability of Detection (POD)) and (lowest False Alarm Ratio (FAR)) events among all datasets. However, all four satellite-based precipitation datasets accurately detect (Critical Success Index (CSI) >40%) precipitation and no-precipitation events. The results of this work provide the systematic quantification of IMERG and GSMaP of satellite precipitation products over Nepal using station observations and delivers a helpful statistical basis for the selection of these datasets for future scientific research.

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

Journal of Space Weather and Space Climate ◽

10.1051/swsc/2019025 ◽

2019 ◽

Vol 9 ◽

pp. A27

Author(s):

Marlon Núñez ◽

Teresa Nieves-Chinchilla ◽

Antti Pulkkinen

Keyword(s):

Quantitative Assessment ◽

Extreme Ultraviolet ◽

Time History ◽

Probability Of Detection ◽

X Rays ◽

Critical Success ◽

Out Of Sample ◽

Overall Performance ◽

Critical Success Index ◽

Success Index

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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Evaluation of Precipitation Estimates CMORPH-CRT on Regions of Mexico with Different Climates

Water ◽

10.3390/w11081722 ◽

2019 ◽

Vol 11 (8) ◽

pp. 1722

Author(s):

José L. Bruster-Flores ◽

Ruperto Ortiz-Gómez ◽

Adrian L. Ferriño-Fierro ◽

Víctor H. Guerra-Cobián ◽

Dagoberto Burgos-Flores ◽

...

Keyword(s):

Absolute Error ◽

Probability Of Detection ◽

Rain Gauges ◽

Critical Success ◽

Lack Of Information ◽

Precipitation Estimates ◽

Global Coverage ◽

Climate Prediction Center ◽

Critical Success Index ◽

Precipitation Events

Satellite-based precipitation (SBP) products with global coverage have the potential to overcome the lack of information in places where there are no rain gauges to perform hydrological analyses; however, it is necessary to evaluate the reliability of the SBP products. In this study, we evaluated the performance of the Climate Prediction Center morphing technique with corrected bias (CMORPH-CRT) product in 14 sites in Mexico. The evaluation was carried out using two approaches: (1) using categorical metrics that include indicators of probability of detection (POD), false alarm rate (FAR), critical success index (CSI), and frequency bias index (FBI); and (2) through statistical indicators such as the mean absolute error (MAE), root mean square error (RMSE), relative bias (RB), and correlation coefficient (CC). The analysis was carried out with two levels of temporal aggregation: 30 min and daily. The results indicate that the CMORPH-CRT product overestimates the number of precipitation events in most cases since FBI values greater than 1 in 78.6% of analyzed stations were obtained. Also, we obtained CC values in the range of 0.018 to 0.625, which implied weak to moderate correlations, and found that in all stations, the CMORPH-CRT product overestimates the precipitation (RB > 0).

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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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A Novel Approach for the Detection of Developing Thunderstorm Cells

Remote Sensing ◽

10.3390/rs11040443 ◽

2019 ◽

Vol 11 (4) ◽

pp. 443 ◽

Cited By ~ 6

Author(s):

Richard Müller ◽

Stéphane Haussler ◽

Matthias Jerg ◽

Dirk Heizenreder

Keyword(s):

Water Vapor ◽

Main Idea ◽

Probability Of Detection ◽

Base Case ◽

Convective Clouds ◽

Critical Success ◽

Novel Approach ◽

Forecast Lead Time ◽

Spatio Temporal ◽

Atmospheric Motion Vectors

This study presents a novel approach for the early detection of developing thunderstorms. To date, methods for the detection of developing thunderstorms have usually relied on accurate Atmospheric Motion Vectors (AMVs) for the estimation of the cooling rates of convective clouds, which correspond to the updraft strengths of the cloud objects. In this study, we present a method for the estimation of the updraft strength that does not rely on AMVs. The updraft strength is derived directly from the satellite observations in the SEVIRI water vapor channels. For this purpose, the absolute value of the vector product of spatio-temporal gradients of the SEVIRI water vapor channels is calculated for each satellite pixel, referred to as Normalized Updraft Strength (NUS). The main idea of the concept is that vertical updraft leads to NUS values significantly above zero, whereas horizontal cloud movement leads to NUS values close to zero. Thus, NUS is a measure of the strength of the vertical updraft and can be applied to distinguish between advection and convection. The performance of the method has been investigated for two summer periods in 2016 and 2017 by validation with lightning data. Values of the Critical Success Index (CSI) of about 66% for 2016 and 60% for 2017 demonstrate the good performance of the method. The Probability of Detection (POD) values for the base case are 81.8% for 2016 and 89.2% for 2017, respectively. The corresponding False Alarm Ratio (FAR) values are 22.6% (2016) and 36.4% (2017), respectively. In summary, the method has the potential to reduce forecast lead time significantly and can be quite useful in regions without a well-maintained radar network.

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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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Spatial and temporal evaluation of multiple gridded precipitation datasets over complex topography and variable climate of Turkey

10.5194/egusphere-egu21-14239 ◽

2021 ◽

Author(s):

Gokcen Uysal ◽

Hamed Hafizi ◽

Ali Arda Sorman

Keyword(s):

Poor Performance ◽

Temporal Consistency ◽

Precipitation Data ◽

Precipitation Estimates ◽

Dry Seasons ◽

Spatio Temporal ◽

Autumn And Winter ◽

Development And Management ◽

Precipitation Events ◽

Better Than

<p>Evaluation of problems related to water resources development and management require accurate precipitation estimates. Although ground-based stations provide direct physical measurement of precipitation, the accuracy of gauge-based precipitation data in terms of quality and spatial pattern may still be controversial. On the other hand, Gridded Precipitation Datasets (GPDs) provide high spatial and temporal precipitation estimates. GPDs are continuously changing with the improving technology and updating of retrospective algorithms, but they still need to be assessed over different regions both in space and time before being used for hydro-climatic studies. This study attempts to evaluate the spatio-temporal consistency of 13 different GPDs (CPCv1, MSWEPv2.2, ERA5, CHIRPSv2.0, CHIRPv2.0, IMERGHHFv06, IMERGHHEv06, IMERGHHLv06, TMPA-3b42v07, TMPA-3b42RTv07, PERSIANN-CDR, PERSIANN-CCS and PERSIANN) over Turkey which is a country characterized by diverse climate and complex terrain. The evaluation is performed for daily and monthly time scales considering the entire period of 2015-2019 as well as seasonal (spring, summer, autumn and winter) variability. Precipitation data from 130 stations are provided as reference data for point-to-grid comparison of GPDs. The modified Kling Gupta Efficiency (KGE) is selected for qualitative analysis whereas the Hanssen&#8211;Kuipers Score (HKS) is used to identify the ability of GPDs for capturing various precipitation events. The Probability Density Function (PDF) is selected to evaluate the intensity frequency of 13 GPDs for individual daily-based precipitation events. The results indicate that all GPDs have a median KGE performance ranging between -0.11 and 0.53 for daily precipitation while their performance increases in the monthly case (median KGE from 0.16 to 0.82). Gauge-corrected GPDs exhibit slightly better results over the uncorrected datasets in comparison with ground observations. GPDs from multi-source merging perform better than only satellite-based and reanalysis precipitation datasets. Among uncorrected GPDs, ERA5 and CHIRPv2.0 perform better while PERSIANN perform worse in all conditions. MSWEPv2.2 suffers from high-altitude conditions during winter and CHIRPSv2.0 shows poor performance during dry seasons. On the overall, MSWEPv2.2 performs better than CHIRPSv2.0 during daily/monthly, while CHIRPv2.0 performs better than CHIRPSv2.0 for daily time scale.</p>

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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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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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