scholarly journals A comparison between high-resolution satellite precipitation estimates and gauge measured data: case study of Gorganrood basin, Iran

2018 ◽  
Vol 67 (3) ◽  
pp. 236-251 ◽  
Author(s):  
Donya Dezfooli ◽  
Banafsheh Abdollahi ◽  
Seyed-Mohammad Hosseini-Moghari ◽  
Kumars Ebrahimi
Keyword(s):  
Pearson Correlation ◽  
Absolute Error ◽  
Probability Of Detection ◽  
Critical Success ◽  
Precipitation Estimates ◽  
Seasonal Basis ◽  
Precipitation Gauge ◽  
Critical Success Index ◽  
Trmm 3B42

Abstract The aim of this paper is to evaluate the accuracy of the precipitation data gathered from satellites including PERSIANN, TRMM-3B42V7, TRMM-3B42RTV7, and CMORPH, over Gorganrood basin, Iran. The data collected from these satellites (2003–2007) were then compared with precipitation gauge observations at six stations, namely, Tamar, Ramiyan, Bahlakeh-Dashli, Sadegorgan, Fazel-Abad, and Ghaffar-Haji. To compare these two groups, mean absolute error (MAE), bias, root mean square error (RMSE), and Pearson correlation coefficient criteria were calculated on daily, monthly, and seasonal basis. Furthermore, probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were calculated for these datasets. Results indicate that, on a monthly scale, the highest correlation between observed and satellite-gathered data calculated is 0.404 for TRMM-3B42 at Bahlakeh-Dashli station. At a seasonal scale, the highest correlation is calculated for winter data and using PERSIANN data, while for the other seasons, TRMM-3B42 data showed the best correlation with observed data. The high values of RMSE and MAE for winter data showed that the satellites provided poor estimations at this season. The best and the worst values of RMSE for studied satellites belonged to Sadegorgan and Ramiyan stations, respectively. Furthermore, the PERSIANN gains a better CSI and POD while TRMM-3B42V7 showed a better FAR.

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

Water ◽  
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).

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

Water ◽  
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.

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

Water ◽  
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.

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

Jalawaayu ◽  
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.

scholarly journals Validation of rainfall data estimated by GPM satellite on Southern Amazon region

2019 ◽  
Vol 14 (1) ◽  
pp. 1
Author(s):  
Luiz Octavio Fabricio dos Santos ◽  
Carlos Alexandre Santos Querino ◽  
Juliane Kayse Albuquerque da Silva Querino ◽  
Altemar Lopes Pedreira Junior ◽  
Aryanne Resende de Melo Moura ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Absolute Error ◽  
High Rate ◽  
Satellite Precipitation ◽  
Precipitation Measurement ◽  
Surface Data ◽  
Precipitation Estimates ◽  
Amazonas State ◽  
Global Precipitation Measurement ◽  
Global Precipitation

Rainfall is a meteorological variable of great importance for hydric balance and for weather studies. Rainfall estimation, carried out by satellites, has increased the climatological dataset related to precipitation. However, the accuracy of these data is questionable. This paper aimed to validate the estimates done by the Global Precipitation Measurement (GPM) satellite for the mesoregion of Southern Amazonas State, Brazil. The surface data were collected by the National Water Agency – ANA and National Institute of Meteorology – INMET, and is available at both institutions’ websites. The satellite precipitation data were accessed directly from the NASA webpage. Statistical analysis of Pearson correlation was used, as well as the Willmott’s “d” index and errors from the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). The GPM satellite satisfactorily estimated the precipitation, once it had correlations above 73% and high Willmott coefficients (between 0.86 and 0.97). The MAE and RMSE showed values that varied from 36.50 mm to 72.49 mm and 13.81 mm to 71.76 mm, respectively. Seasonal rain variations are represented accordingly. In some cases, either an underestimation or an overestimation of the rain data was observed. In the yearly totals, a high rate of similarity between the estimated and measured values was observed. We concluded that the GPM-based multi-satellite precipitation estimates can be used, even though they are not 100% reliable. However, adjustments in calibration for the region are necessary and recommended.

scholarly journals EVALUASI DATA CLIMATE HAZARDS GROUP INFRARED PRECIPITATION WITH STATION (CHIRPS) DENGAN DATA PEMBANDING AUTOMATIC WEATHER STATIONS (AWS) DALAM MENGESTIMASI CURAH HUJAN HARIAN DI PROVINSI PAPUA BARAT

2021 ◽  
Vol 10 (1) ◽  
pp. 64
Author(s):  
Budiyono Budiyono ◽  
Arif Faisol
Keyword(s):  
Pearson Correlation ◽  
Daily Rainfall ◽  
Absolute Error ◽  
Satellite Observation ◽  
Probability Of Detection ◽  
T Test ◽  
Data Recording ◽  
Significant Difference ◽  
West Papua ◽  
Weather Stations

This research aims to evaluate the CHIRPS data in estimating daily rainfall in West Papua compared with automatic weather stations (AWS) data recording. The data used in this research are daily CHIRPS data and AWS daily data recording 1996 to 2020 from AWS Rendani–Manokwari, AWS Jefman–Raja Ampat, AWS Torea–Fakfak, and AWS Kaimana–Kaimana. CHIRPS data were evaluated using the Point to Pixel method based on numerical and categorical parameters i.e., root mean square error (RMSE), mean error (ME), mean absolute error (MAE), Pearson correlation (r), probability of detection (POD), critical success index (CSI), and T-test. The research showed that CHIRPS had a significant difference to AWS data in estimating daily rainfall in West Papua based on a T-test. However CHIRPS has a moderate accuracy in estimating daily rainfall in West Papua with RMSE = 8.59 mm, ME=2.75 mm, and MAE = 5.15 mm and had a moderate positive correlation with AWS data with r= 0.43. Besides, CHIRPS has good accuracy in detecting rain events in West Papua indicated by a POD = 0.72 and CSI = 0.43. Therefore, CHIRPS data can be used as an alternative solution for providing rainfall data in West Papua.   Keywords:  satellite observation, rainfall predictor, point to pixel 

scholarly journals APPLICATION OF A SPATIAL VERIFICATION METHOD TO GFS PRECIPITATION FORECASTS

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.

scholarly journals Conditional Generative Adversarial Networks (cGANs) for Near Real-Time Precipitation Estimation from Multispectral GOES-16 Satellite Imageries—PERSIANN-cGAN

2019 ◽  
Vol 11 (19) ◽  
pp. 2193 ◽  
Author(s):  
Negin Hayatbini ◽  
Bailey Kong ◽  
Kuo-lin Hsu ◽  
Phu Nguyen ◽  
Soroosh Sorooshian ◽  
...  
Keyword(s):  
Mean Squared Error ◽  
Probability Of Detection ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Critical Success ◽  
Adversarial Network ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation

In this paper, we present a state-of-the-art precipitation estimation framework which leverages advances in satellite remote sensing as well as Deep Learning (DL). The framework takes advantage of the improvements in spatial, spectral and temporal resolutions of the Advanced Baseline Imager (ABI) onboard the GOES-16 platform along with elevation information to improve the precipitation estimates. The procedure begins by first deriving a Rain/No Rain (R/NR) binary mask through classification of the pixels and then applying regression to estimate the amount of rainfall for rainy pixels. A Fully Convolutional Network is used as a regressor to predict precipitation estimates. The network is trained using the non-saturating conditional Generative Adversarial Network (cGAN) and Mean Squared Error (MSE) loss terms to generate results that better learn the complex distribution of precipitation in the observed data. Common verification metrics such as Probability Of Detection (POD), False Alarm Ratio (FAR), Critical Success Index (CSI), Bias, Correlation and MSE are used to evaluate the accuracy of both R/NR classification and real-valued precipitation estimates. Statistics and visualizations of the evaluation measures show improvements in the precipitation retrieval accuracy in the proposed framework compared to the baseline models trained using conventional MSE loss terms. This framework is proposed as an augmentation for PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network- Cloud Classification System) algorithm for estimating global precipitation.

scholarly journals Exploring the Usefulness of Downscaling Free Forecasts from the Warn-on-Forecast System

2021 ◽  
Keyword(s):  
Forecast Model ◽  
Probability Of Detection ◽  
Forecast System ◽  
Local Maxima ◽  
Critical Success ◽  
Object Based ◽  
Verification Methods ◽  
Horizontal Grid ◽  
Critical Success Index ◽  
Eleven Case

Abstract The National Severe Storms Lab (NSSL) Warn-on-Forecast System (WoFS) is an experimental real-time rapidly-updating convection-allowing ensemble that provides probabilistic short-term thunderstorm forecasts. This study evaluates the impacts of reducing the forecast model horizontal grid spacing Δx from 3 km to 1.5 km on the WoFS deterministic and probabilistic forecast skill, using eleven case days selected from the 2020 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiment (SFE). Verification methods include (i) subjective forecaster impressions; (ii) a deterministic object-based technique that identifies forecast reflectivity and rotation track storm objects as contiguous local maxima in the composite reflectivity and updraft helicity fields, respectively, and matches them to observed storm objects; and (iii) a recently developed algorithm that matches observed mesocyclones to mesocyclone probability swath objects constructed from the full ensemble of rotation track objects. Reducing Δx fails to systematically improve deterministic skill in forecasting reflectivity object occurrence, as measured by critical success index (CSIDET), a metric that incorporates both probability of detection (PODDET) and false alarm ratio (FARDET). However, compared to the Δx = 3 km configuration, the Δx = 1.5 km WoFS shows improved mid-level mesocyclone detection, as evidenced by its statistically significant (i) higher CSIDET for deterministic mid-level rotation track objects and (ii) higher normalized area under the performance diagram curve (NAUPDC) score for probability swath objects. Comparison between Δx = 3 km and Δx = 1.5 km reflectivity object properties reveals that the latter have 30% stronger mean updraft speeds, 17% stronger median 80-m winds, 67% larger median hail diameter, and 28% higher median near-storm-maximum 0-3 km storm-relative helicity.

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

2016 ◽  
Vol 33 (1) ◽  
pp. 61-80 ◽  
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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