The launch of NOAA’s latest generation of geostationary satellites known as the Geostationary Ope... more The launch of NOAA’s latest generation of geostationary satellites known as the Geostationary Operational Environmental Satellite (GOES)-R Series has opened new opportunities in quantifying precipitation rates. Recent efforts have strived to utilize these data to improve space-based precipitation retrievals. The overall objective of the present work is to carry out a detailed error budget analysis of the improved Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for GOES-R and the passive microwave (MW) combined (MWCOMB) precipitation dataset used to calibrate it with an aim to provide insights regarding strengths and weaknesses of these products. This study systematically analyzes the errors across different climate regions and also as a function of different precipitation types over the conterminous United States. The reference precipitation dataset is Ground-Validation Multi-Radar Multi-Sensor (GV-MRMS). Overall, MWCOMB reveals smaller errors as compared to...
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014
Rain is one of the major components of water cycle; extreme rain events can cause destruction and... more Rain is one of the major components of water cycle; extreme rain events can cause destruction and misery due to flash flood and droughts. Therefore, assessing rainfall at high temporal and spatial resolution is of fundamental importance which can be achieved only by satellite remote sensing. Though there are many algorithms developed for estimation of rainfall using satellite data, they suffer from various drawbacks. One such challenge in satellite rainfall estimation is to detect rain and no-rain areas properly. To address this problem, in the present study we have used the Support Vector Machines (SVM). It is significant to note that this is the first study to report the utility of SVM in detecting rain and no-rain areas. The developed SVM based index performance has been evaluated by comparing with two most popular rain detection methods used for Indian regions i.e. Simple <i>TIR</i> threshold used in Global Precipitation Index (GPI) technique and <i>Roca</i&...
The high spatial, temporal, and spectral resolutions from the new generation of GEO satellites pr... more The high spatial, temporal, and spectral resolutions from the new generation of GEO satellites provide opportunities to map precipitation more accurately and enhance our understanding of precipitation processes. The research question addressed in this study is: Which predictors derived from satellite observations are significant in estimating the occurrence of a given precipitation process? Several indices from the Advanced Baseline Imager (ABI) sensor onboard the Geostationary Observing Environmental Satellite (GOES)-16 are derived and matched with surface precipitation types from the Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) system across the conterminous United States (CONUS). A machine learning (ML) based Random Forest (RF) classification is developed with several categories of predictors, such as ABI brightness temperatures (Tb) from five channels, spectral channel differences and textures, and environmental variables from the Rapid Refresh numerical forecast model (...
Quarterly Journal of the Royal Meteorological Society, 2021
"Precipitation is one of the most important components of the global water and energy cycles, whi... more "Precipitation is one of the most important components of the global water and energy cycles, which together regulate the climate system. Future changes in precipitation patterns related to climate change are likely to bear the largest impacts on society. The new generation of geostationary Earth orbit (GEO) satellites provide high-resolution observations and opportunities to improve our understanding of precipitation processes. This study contributes to improved precipitation characterization and retrievals from space by identifying precipitation types (e.g., convective, stratiform) with multi-spectral observations from the Advanced Baseline Imager (ABI) sensor onboard the GOES-16 satellite. A machine learning-based classification model is developed by deriving a comprehensive set of features using five ABI channels and numerical weather prediction observations, and trained with the Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) system used as a benchmark. The developed prognostic model shows skillful performance in identifying the occurrence/non-occurrence of precipitation (accuracy of 97%; Kappa coefficient of 0.9) and precipitation processes, with an overall classification accuracy of 76% and Kappa coefficient of 0.56. Challenges exist in separating convective and tropical from other precipitation types. It is suggested to utilize probabilities instead of deterministically separating precipitation types especially in regions with uncertain classifications."
Quarterly Journal of the Royal Meteorological Society, 2021
"Improvements in remote sensing capability and improvements in artificial intelligence have creat... more "Improvements in remote sensing capability and improvements in artificial intelligence have created significant opportunities to advance understanding of precipitation processes. While highly advanced Machine Learning (ML) techniques improve the accuracy of precipitation retrievals, how these observations contribute to our understanding of precipitation processes remains an underexplored research question. In a companion manuscript, a precipitation type prognostic ML model is developed by deriving predictors from the Advanced Baseline Imager (ABI) sensor onboard Geostationary Observing Environmental Satellite (GOES)-16. In this study, these predictors are linked to different precipitation processes. It is observed that satellite observations are important in separating Rain and No-Rain areas. For stratiform precipitation types, predictors related to atmospheric moisture content, such as relative humidity and precipitable water, are the most important predictors, while for convective types, predictors such as 850-500hPa lapse-rate and Convective Available Potential Energy (CAPE) are more important. The diagnostic analysis confirms the benefit of spatial textures derived from ABI observations to improve the classification accuracy. It is recommended to combine the heritage water vapor channel T6.2 with the IR T11.2 channel for improved precipitation classification. Overall, this study provides guidance to atmospheric and remote sensing scientists on a large array of predictors that can be used from geostationary satellites and multispectral sensors for precipitation studies."
The launch of NOAA’s latest generation of geostationary satellites known as the Geostationary Ope... more The launch of NOAA’s latest generation of geostationary satellites known as the Geostationary Operational Environmental Satellite (GOES)-R Series has opened new opportunities in quantifying precipitation rates. Recent efforts have strived to utilize these data to improve space-based precipitation retrievals. The overall objective of the present work is to carry out a detailed error budget analysis of the improved Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for GOES-R and the passive microwave (MW) combined (MWCOMB) precipitation dataset used to calibrate it with an aim to provide insights regarding strengths and weaknesses of these products. This study systematically analyzes the errors across different climate regions and also as a function of different precipitation types over the conterminous United States. The reference precipitation dataset is Ground-Validation Multi-Radar Multi-Sensor (GV-MRMS). Overall, MWCOMB reveals smaller errors as compared to SCaMPR. However, the analysis indicated that that the major portion of error in SCaMPR is propagated from the MWCOMB calibration data. The major challenge starts with poor detection from MWCOMB, which propagates in SCaMPR. In particular, MWCOMB misses 90% of cool stratiform precipitation and the overall detection score is around 40%. The ability of the algorithms to quantify precipitation amounts for the Warm Stratiform, Cool Stratiform, and Tropical/Stratiform Mix categories is poor compared to the Convective and Tropical/Convective Mix categories with additional challenges in complex terrain regions. Further analysis showed strong similarities in systematic and random error models with both products. This suggests that the potential of high-resolution GOES-R observations remains underutilized in SCaMPR due to the errors from the calibrator MWCOMB.
Continuous availability of a variety of satellite and reanalysis rainfall products have triggered... more Continuous availability of a variety of satellite and reanalysis rainfall products have triggered the use of such products as an alternate source of rainfall data in sparsely gauge networked areas. However, before utilizing them a detailed validation of these datasets are essential to have some level of guarantee. In many parts of Africa in general and most parts of Ethiopia particularly in the lowland areas, gauge stations are very sparse and unevenly distributed. In addition, due to the nature of complex topography and geographical location, Ethiopian rainfall shows high variability both temporally and spatially. In view of the above, the present study is aimed at statistically evaluating such rainfall products across different rainfall regimes (regions with different rainfall characteristics as defined by National Meteorological Agency (NMA) of Ethiopia). In the current study, five satellite and two reanalysis rainfall products such as African Rainfall Climatology version 2 (ARC2), Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT), Tropical Rainfall Measuring Mission-3B43 version 7 (TRMM 3B43v7), Climate Prediction Center Morphing Technique (CMORPH), Climate Hazards Group Infrared Precipitation with Stations version 2 (CHIRPSv2), the Climate Forecast System Reanalysis (CFSR) and the European Center for Medium Range Weather Forecast Reanalysis (ERA-Interim) are considered based on their spatial coverage, spatial resolution, temporal resolution, latency period and length of data records. Evaluation is done at monthly and seasonal time scales against the observed gauge rainfall data provided by the National Meteorological Agency of Ethiopia across entire Ethiopia in two different manners, first by considering the entire country as one homogeneous unit and secondly in a distributed manner across the three rainfall regimes of Ethiopia. The obtained results show that: (i) CHIRPSv2 and TRMM 3B43v7 show better performance during June to September (the main rainfall season) and during February to May (the smaller rainfall season) in regimes 1 and 2. (ii) In regime 3 these products show good performance from October to November (smaller rainy season of this regime) and March to May (main rainy season of this regime); (iii).CMORPH, TAMSAT and ARC2 show moderate performance in all three regimes; (iv) CFSR and ERA-Interim exhibit poor performance in all rainfall regimes. Overall, the detailed analysis of statistical evaluation results of the rainfall products at monthly timescale shows that CHIRPSv2 performs comparatively better than the other tested rainfall products across all rainfall regimes. However, the best performance of CHIRPSv2 is obtained in regime 2 followed by regime 1 and regime 3.
The present article reports an improvement in the INSAT Multispectral Rainfall Algorithm which is... more The present article reports an improvement in the INSAT Multispectral Rainfall Algorithm which is currently operational in the Indian Meteorological Department (IMD). The proposed Modified-IMSRA (M-IMSRA) algorithm deviates from original IMSRA in two ways: first is by improvement in rain/no-rain area detection scheme using a Multi-Index Rain Detection (MIRD) index; second is based on the climate region-wise correction through Least Absolute Shrinkage and Selection Operator (LASSO) models developed for each climate regions using rainfall (obtained based on first improvement) and static topographic variables extracted from Digital Elevation Model (DEM). The overall results indicate that the M-IMSRA is performing better than the IMSRA in all climatic regions when compared with the IMD gridded gauge data. However, the improvement is not uniform in all the regions. The inclusion of the MIRD index led to considerable improvement in M-IMSRA-based rainfall estimates mainly in the arid regions. Likewise, the results obtained after the LASSO regression corrections indicate that they are necessary only for the orographic regions where significant improvements are observed in the rainfall estimates. Finally, the inter-comparison of the simple hybrid M-IMSRA estimates with Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 and TRMM 3B42-RT V7 illustrates that the M-IMSRA performs nearly as well as even better (except in terms of Correlation Coefficient) than the complex multi-satellite-based rainfall estimates in all the climate regions of India. Considering the above results, it can be said that the performance of simple hybrid algorithms such as IMSRA can be improved to match the quality or even outperform complex multi-satellite rainfall estimates by incorporating appropriate corrections.
In this article, a new approach called Multi-Index Rain Detection (MIRD) is suggested for regiona... more In this article, a new approach called Multi-Index Rain Detection (MIRD) is suggested for regional rain area detection and was tested for India using Kalpana-1 satellite data. The approach was developed based on the following hypothesis: better results should be obtained for combined indices than an individual index. Different combinations (scenarios) were developed by combining six commonly used rain detection indices using AND and OR logical connectives. For the study region, an optimal rain area detection scenario and optimal threshold values of the indices were found through a statistical multi-decision-making technique called the Technique for Order Preference by Similarity Ideal Solution (TOPSIS). The TOPSIS analysis was carried out based on independent categorical statistics like probability of detection, probability of no detection, and Heidke skill score. It is noteworthy that for the first time in literature, an attempt has been made (through sensitivity analysis) to understand the influence of the proportion of rain/no-rain pixels in the calibration/ validation dataset on a few commonly used statistics. Thus, the obtained results have been used to identify the above-mentioned independent categorical statistics. Based on the results obtained and the validation carried out with different independent datasets, scenario 8 (TIR t , 260 K and TIR t 2 WV t , 19 K, where TIR t and WV t are the brightness temperatures from thermal IR and water vapor, respectively) is found to be an optimal rain detection index. The obtained results also indicate that the texture-based indices [standard deviation and mean of 5 3 5 pixels at time t (mean 5)] did not perform well, perhaps because of the coarse resolution of Kalpana-1 data. It is also to be noted that scenario 8 performs much better than the Roca method used in the Indian National Satellite (INSAT) Multispectral Rainfall Algorithm (IMSRA) developed for India.
Floods due to extreme rain events pose a major threat not only to the human life but also have hu... more Floods due to extreme rain events pose a major threat not only to the human life but also have huge impact on socioeconomic growth of agricultural based countries like India, which has highly varied climate and topography. Hence, for disaster preparedness and flood forecasting over large river basins, there is a need for proper knowledge on space and time distribution of rainfall in real time basis. This can be achieved only based on near real time satellite rainfall estimates (SRE) as rain gauges are sparsely distributed over the country and Doppler Weather Radars (DWR) are highly expensive. However, verification of these near real time SRE methods and products are a prerequisite to apply SRE for flood prediction. Therefore, a study has been conducted to validate two Near Real Time High Resolution satellite Precipitation Products (NRT-HRPP’s) namely, Tropical Rainfall Measuring Mission- Real Time (TRMM-3B42 RT) and Insat-Multispectral-Rainfall-Algorithm (IMSRA) estimates over India...
Rain is one of the major components of water cycle; extreme rain events can cause destruction and... more Rain is one of the major components of water cycle; extreme rain events can cause destruction and misery due to flash flood and droughts. Therefore, assessing rainfall at high temporal and spatial resolution is of fundamental importance which can be achieved only by satellite remote sensing. Though there are many algorithms developed for estimation of rainfall using satellite data, they suffer from various drawbacks. One such challenge in satellite rainfall estimation is to detect rain and no-rain areas properly. To address this problem, in the present study we have used the Support Vector Machines (SVM). It is significant to note that this is the first study to report the utility of SVM in detecting rain and no-rain areas. The developed SVM based index performance has been evaluated by comparing with two most popular rain detection methods used for Indian regions i.e. Simple TIR threshold used in Global Precipitation Index (GPI) technique and Roca method used in Insat Multi Spectral Rainfall Algorithm (IMSRA). Performance of the above considered indices has been analyzed by considering various categorical statistics like Probabil ity of Detection (POD), Probability of no-rain detection (POND), Accuracy, Bias, False Alarm Ratio (FAR) and Heidke Skill Score (HSS). The obtained results clearly show that the new SVM based index performs much better than the earlier indices.
The main objective of this study is to validate and inter-compare two Near-Real-Time Satellite Ra... more The main objective of this study is to validate and inter-compare two Near-Real-Time Satellite Rainfall Estimates (NRT-SREs): INSAT Multispectral Rainfall Algorithm (IMSRA, simple blended product) and TMPA 3B42-RT V7 (3B42-RT, multisatellite product) across India. This study aims to provide some insight into the error characteristics of both the NRT-SREs to the algorithm developers and end users by inter-comparing the daily rainfall estimates during the southwest monsoon period of 2010–2013. This study utilizes various volu-metric statistics and categorical statistics to understand and evaluate the performance of NRT-SREs in terms of both spatial and volumetric error characteristics (hit, miss, and false error) at different rainfall thresholds across different Köppen–Geiger climate regions of India using the gridded gauge data provided by Indian Meteorological Department as reference dataset. A detailed statistical evaluation shows that the 3B42-RT performs comparatively better than the IMSRA across India. The results indicate that both IMSRA and 3B42-RT have a general tendency of overestimating the low rainfall rates (0– 2.5 mm/day) and underestimating the high rainfall rates (> 35.5 mm/day). At lower threshold values (0 and 2.5 mm/day), it is found that the miss error is dominant in IMSRA, whereas the false error is dominant in 3B42-RT. As the threshold increases (7.5 and 35.5 mm/day), both the miss and false errors increase in both SREs. Additionally, the spatial analysis of the results clearly indicate that the performance of the tested NRT-SREs is not uniform across different climatic regions, an important aspect to be considered for development/ improvement of the tested NRT-SRE algorithms.
Estimating accurate rainfall is very much needed for proper management of water resources. In cas... more Estimating accurate rainfall is very much needed for proper management of water resources. In case of scarcity in rain gauge stations as prevailing in most developing countries, accurate estimation becomes near impossible. To overcome this limitation, satellite data can be used which provides an alternate solution. However, there are still lots of challenges involved in satellite based rainfall estimations. One such challenge is to accurately detect the rain and no-rain pixels in a satellite image. As a solution, a new index i.e., TIRt<260K and TIRt -WVt<19K (Upadhyaya and Ramsankaran, 2014) has been developed for detecting rain and no-rain pixels in Kalpana-1 satellite images. This article presents the results of an evaluation study of the above mentioned index over Indian region. This study has been conducted for the south west monsoon season of the year 2013 using TRMM-2A25 rainfall rates as reference dataset. The newly developed index’s performance has been checked by comparing with two most popular rain detection methods used for Indian regions i.e. Simple TIR threshold used in Global Precipitation Index (GPI) technique and Roca method used in Insat Multi Spectral Rainfall Algorithm (IMSRA). Performance of the above considered indices has been analyzed by considering various categorical statistics like Probability of Detection (POD), Probability of no-rain detection (POND), Accuracy, Bias, False Alarm Ratio (FAR) and Heidke Skill Score (HSS). The obtained results clearly show that the new index performs much better than the earlier indices.
Rain is one of the major components of water cycle; extreme rain events can cause destruction and... more Rain is one of the major components of water cycle; extreme rain events can cause destruction and misery due to flash flood and droughts. Therefore, assessing rainfall at high temporal and spatial resolution is of fundamental importance which can be achieved only by satellite remote sensing. Conventional method like rain gauges is sparsely and unevenly distributed primarily over the land and with a coarse frequency of measurement. Whereas ground based radar is another method which is having high temporal but low spatial resolution and is considered to be too expensive for developing countries. Most of the satellite data is available free of cost which makes the rainfall estimation using satellite remote sensing data, a cost effective method. This paper reviews current and earlier techniques on rainfall estimation from satellite remote sensing observations.
Transportation Research Record Journal of the Transportation Research Board, Jan 11, 2015
ABSTRACT Gap acceptance predictions provide very important inputs for performance evaluation and ... more ABSTRACT Gap acceptance predictions provide very important inputs for performance evaluation and safety analysis of uncontrolled intersections and pedestrian midblock crossings. This paper deals with the application of Support Vector Machines (SVM), in understanding and classifying gaps at these facilities. SVMs are supervised learning technique originated from statistical learning theory and are widely used for classification and regression. The objective of this paper is to examine the feasibility of SVM in analyzing gap acceptance by comparing its results with existing statistical methods. To accomplish this objective, SVM and Binary Logit Models (BLM) were developed and compared using data collected at three different types of uncontrolled sections. SVM performance was found to be comparable with BLM in all cases and better in a few cases. Also the categorical statistics and skill scores used for validating the gap acceptance data revealed that SVM performs reasonably well. Thus the SVM technique can be used to classify and predict the accepted and rejected gap values based on speed and distance of the oncoming vehicles. This technique can be used in safety and advanced warning systems for the vehicles and pedestrians waiting to cross the main line stream vehicles.
The launch of NOAA’s latest generation of geostationary satellites known as the Geostationary Ope... more The launch of NOAA’s latest generation of geostationary satellites known as the Geostationary Operational Environmental Satellite (GOES)-R Series has opened new opportunities in quantifying precipitation rates. Recent efforts have strived to utilize these data to improve space-based precipitation retrievals. The overall objective of the present work is to carry out a detailed error budget analysis of the improved Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for GOES-R and the passive microwave (MW) combined (MWCOMB) precipitation dataset used to calibrate it with an aim to provide insights regarding strengths and weaknesses of these products. This study systematically analyzes the errors across different climate regions and also as a function of different precipitation types over the conterminous United States. The reference precipitation dataset is Ground-Validation Multi-Radar Multi-Sensor (GV-MRMS). Overall, MWCOMB reveals smaller errors as compared to...
ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2014
Rain is one of the major components of water cycle; extreme rain events can cause destruction and... more Rain is one of the major components of water cycle; extreme rain events can cause destruction and misery due to flash flood and droughts. Therefore, assessing rainfall at high temporal and spatial resolution is of fundamental importance which can be achieved only by satellite remote sensing. Though there are many algorithms developed for estimation of rainfall using satellite data, they suffer from various drawbacks. One such challenge in satellite rainfall estimation is to detect rain and no-rain areas properly. To address this problem, in the present study we have used the Support Vector Machines (SVM). It is significant to note that this is the first study to report the utility of SVM in detecting rain and no-rain areas. The developed SVM based index performance has been evaluated by comparing with two most popular rain detection methods used for Indian regions i.e. Simple <i>TIR</i> threshold used in Global Precipitation Index (GPI) technique and <i>Roca</i&...
The high spatial, temporal, and spectral resolutions from the new generation of GEO satellites pr... more The high spatial, temporal, and spectral resolutions from the new generation of GEO satellites provide opportunities to map precipitation more accurately and enhance our understanding of precipitation processes. The research question addressed in this study is: Which predictors derived from satellite observations are significant in estimating the occurrence of a given precipitation process? Several indices from the Advanced Baseline Imager (ABI) sensor onboard the Geostationary Observing Environmental Satellite (GOES)-16 are derived and matched with surface precipitation types from the Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) system across the conterminous United States (CONUS). A machine learning (ML) based Random Forest (RF) classification is developed with several categories of predictors, such as ABI brightness temperatures (Tb) from five channels, spectral channel differences and textures, and environmental variables from the Rapid Refresh numerical forecast model (...
Quarterly Journal of the Royal Meteorological Society, 2021
"Precipitation is one of the most important components of the global water and energy cycles, whi... more "Precipitation is one of the most important components of the global water and energy cycles, which together regulate the climate system. Future changes in precipitation patterns related to climate change are likely to bear the largest impacts on society. The new generation of geostationary Earth orbit (GEO) satellites provide high-resolution observations and opportunities to improve our understanding of precipitation processes. This study contributes to improved precipitation characterization and retrievals from space by identifying precipitation types (e.g., convective, stratiform) with multi-spectral observations from the Advanced Baseline Imager (ABI) sensor onboard the GOES-16 satellite. A machine learning-based classification model is developed by deriving a comprehensive set of features using five ABI channels and numerical weather prediction observations, and trained with the Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) system used as a benchmark. The developed prognostic model shows skillful performance in identifying the occurrence/non-occurrence of precipitation (accuracy of 97%; Kappa coefficient of 0.9) and precipitation processes, with an overall classification accuracy of 76% and Kappa coefficient of 0.56. Challenges exist in separating convective and tropical from other precipitation types. It is suggested to utilize probabilities instead of deterministically separating precipitation types especially in regions with uncertain classifications."
Quarterly Journal of the Royal Meteorological Society, 2021
"Improvements in remote sensing capability and improvements in artificial intelligence have creat... more "Improvements in remote sensing capability and improvements in artificial intelligence have created significant opportunities to advance understanding of precipitation processes. While highly advanced Machine Learning (ML) techniques improve the accuracy of precipitation retrievals, how these observations contribute to our understanding of precipitation processes remains an underexplored research question. In a companion manuscript, a precipitation type prognostic ML model is developed by deriving predictors from the Advanced Baseline Imager (ABI) sensor onboard Geostationary Observing Environmental Satellite (GOES)-16. In this study, these predictors are linked to different precipitation processes. It is observed that satellite observations are important in separating Rain and No-Rain areas. For stratiform precipitation types, predictors related to atmospheric moisture content, such as relative humidity and precipitable water, are the most important predictors, while for convective types, predictors such as 850-500hPa lapse-rate and Convective Available Potential Energy (CAPE) are more important. The diagnostic analysis confirms the benefit of spatial textures derived from ABI observations to improve the classification accuracy. It is recommended to combine the heritage water vapor channel T6.2 with the IR T11.2 channel for improved precipitation classification. Overall, this study provides guidance to atmospheric and remote sensing scientists on a large array of predictors that can be used from geostationary satellites and multispectral sensors for precipitation studies."
The launch of NOAA’s latest generation of geostationary satellites known as the Geostationary Ope... more The launch of NOAA’s latest generation of geostationary satellites known as the Geostationary Operational Environmental Satellite (GOES)-R Series has opened new opportunities in quantifying precipitation rates. Recent efforts have strived to utilize these data to improve space-based precipitation retrievals. The overall objective of the present work is to carry out a detailed error budget analysis of the improved Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for GOES-R and the passive microwave (MW) combined (MWCOMB) precipitation dataset used to calibrate it with an aim to provide insights regarding strengths and weaknesses of these products. This study systematically analyzes the errors across different climate regions and also as a function of different precipitation types over the conterminous United States. The reference precipitation dataset is Ground-Validation Multi-Radar Multi-Sensor (GV-MRMS). Overall, MWCOMB reveals smaller errors as compared to SCaMPR. However, the analysis indicated that that the major portion of error in SCaMPR is propagated from the MWCOMB calibration data. The major challenge starts with poor detection from MWCOMB, which propagates in SCaMPR. In particular, MWCOMB misses 90% of cool stratiform precipitation and the overall detection score is around 40%. The ability of the algorithms to quantify precipitation amounts for the Warm Stratiform, Cool Stratiform, and Tropical/Stratiform Mix categories is poor compared to the Convective and Tropical/Convective Mix categories with additional challenges in complex terrain regions. Further analysis showed strong similarities in systematic and random error models with both products. This suggests that the potential of high-resolution GOES-R observations remains underutilized in SCaMPR due to the errors from the calibrator MWCOMB.
Continuous availability of a variety of satellite and reanalysis rainfall products have triggered... more Continuous availability of a variety of satellite and reanalysis rainfall products have triggered the use of such products as an alternate source of rainfall data in sparsely gauge networked areas. However, before utilizing them a detailed validation of these datasets are essential to have some level of guarantee. In many parts of Africa in general and most parts of Ethiopia particularly in the lowland areas, gauge stations are very sparse and unevenly distributed. In addition, due to the nature of complex topography and geographical location, Ethiopian rainfall shows high variability both temporally and spatially. In view of the above, the present study is aimed at statistically evaluating such rainfall products across different rainfall regimes (regions with different rainfall characteristics as defined by National Meteorological Agency (NMA) of Ethiopia). In the current study, five satellite and two reanalysis rainfall products such as African Rainfall Climatology version 2 (ARC2), Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT), Tropical Rainfall Measuring Mission-3B43 version 7 (TRMM 3B43v7), Climate Prediction Center Morphing Technique (CMORPH), Climate Hazards Group Infrared Precipitation with Stations version 2 (CHIRPSv2), the Climate Forecast System Reanalysis (CFSR) and the European Center for Medium Range Weather Forecast Reanalysis (ERA-Interim) are considered based on their spatial coverage, spatial resolution, temporal resolution, latency period and length of data records. Evaluation is done at monthly and seasonal time scales against the observed gauge rainfall data provided by the National Meteorological Agency of Ethiopia across entire Ethiopia in two different manners, first by considering the entire country as one homogeneous unit and secondly in a distributed manner across the three rainfall regimes of Ethiopia. The obtained results show that: (i) CHIRPSv2 and TRMM 3B43v7 show better performance during June to September (the main rainfall season) and during February to May (the smaller rainfall season) in regimes 1 and 2. (ii) In regime 3 these products show good performance from October to November (smaller rainy season of this regime) and March to May (main rainy season of this regime); (iii).CMORPH, TAMSAT and ARC2 show moderate performance in all three regimes; (iv) CFSR and ERA-Interim exhibit poor performance in all rainfall regimes. Overall, the detailed analysis of statistical evaluation results of the rainfall products at monthly timescale shows that CHIRPSv2 performs comparatively better than the other tested rainfall products across all rainfall regimes. However, the best performance of CHIRPSv2 is obtained in regime 2 followed by regime 1 and regime 3.
The present article reports an improvement in the INSAT Multispectral Rainfall Algorithm which is... more The present article reports an improvement in the INSAT Multispectral Rainfall Algorithm which is currently operational in the Indian Meteorological Department (IMD). The proposed Modified-IMSRA (M-IMSRA) algorithm deviates from original IMSRA in two ways: first is by improvement in rain/no-rain area detection scheme using a Multi-Index Rain Detection (MIRD) index; second is based on the climate region-wise correction through Least Absolute Shrinkage and Selection Operator (LASSO) models developed for each climate regions using rainfall (obtained based on first improvement) and static topographic variables extracted from Digital Elevation Model (DEM). The overall results indicate that the M-IMSRA is performing better than the IMSRA in all climatic regions when compared with the IMD gridded gauge data. However, the improvement is not uniform in all the regions. The inclusion of the MIRD index led to considerable improvement in M-IMSRA-based rainfall estimates mainly in the arid regions. Likewise, the results obtained after the LASSO regression corrections indicate that they are necessary only for the orographic regions where significant improvements are observed in the rainfall estimates. Finally, the inter-comparison of the simple hybrid M-IMSRA estimates with Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 and TRMM 3B42-RT V7 illustrates that the M-IMSRA performs nearly as well as even better (except in terms of Correlation Coefficient) than the complex multi-satellite-based rainfall estimates in all the climate regions of India. Considering the above results, it can be said that the performance of simple hybrid algorithms such as IMSRA can be improved to match the quality or even outperform complex multi-satellite rainfall estimates by incorporating appropriate corrections.
In this article, a new approach called Multi-Index Rain Detection (MIRD) is suggested for regiona... more In this article, a new approach called Multi-Index Rain Detection (MIRD) is suggested for regional rain area detection and was tested for India using Kalpana-1 satellite data. The approach was developed based on the following hypothesis: better results should be obtained for combined indices than an individual index. Different combinations (scenarios) were developed by combining six commonly used rain detection indices using AND and OR logical connectives. For the study region, an optimal rain area detection scenario and optimal threshold values of the indices were found through a statistical multi-decision-making technique called the Technique for Order Preference by Similarity Ideal Solution (TOPSIS). The TOPSIS analysis was carried out based on independent categorical statistics like probability of detection, probability of no detection, and Heidke skill score. It is noteworthy that for the first time in literature, an attempt has been made (through sensitivity analysis) to understand the influence of the proportion of rain/no-rain pixels in the calibration/ validation dataset on a few commonly used statistics. Thus, the obtained results have been used to identify the above-mentioned independent categorical statistics. Based on the results obtained and the validation carried out with different independent datasets, scenario 8 (TIR t , 260 K and TIR t 2 WV t , 19 K, where TIR t and WV t are the brightness temperatures from thermal IR and water vapor, respectively) is found to be an optimal rain detection index. The obtained results also indicate that the texture-based indices [standard deviation and mean of 5 3 5 pixels at time t (mean 5)] did not perform well, perhaps because of the coarse resolution of Kalpana-1 data. It is also to be noted that scenario 8 performs much better than the Roca method used in the Indian National Satellite (INSAT) Multispectral Rainfall Algorithm (IMSRA) developed for India.
Floods due to extreme rain events pose a major threat not only to the human life but also have hu... more Floods due to extreme rain events pose a major threat not only to the human life but also have huge impact on socioeconomic growth of agricultural based countries like India, which has highly varied climate and topography. Hence, for disaster preparedness and flood forecasting over large river basins, there is a need for proper knowledge on space and time distribution of rainfall in real time basis. This can be achieved only based on near real time satellite rainfall estimates (SRE) as rain gauges are sparsely distributed over the country and Doppler Weather Radars (DWR) are highly expensive. However, verification of these near real time SRE methods and products are a prerequisite to apply SRE for flood prediction. Therefore, a study has been conducted to validate two Near Real Time High Resolution satellite Precipitation Products (NRT-HRPP’s) namely, Tropical Rainfall Measuring Mission- Real Time (TRMM-3B42 RT) and Insat-Multispectral-Rainfall-Algorithm (IMSRA) estimates over India...
Rain is one of the major components of water cycle; extreme rain events can cause destruction and... more Rain is one of the major components of water cycle; extreme rain events can cause destruction and misery due to flash flood and droughts. Therefore, assessing rainfall at high temporal and spatial resolution is of fundamental importance which can be achieved only by satellite remote sensing. Though there are many algorithms developed for estimation of rainfall using satellite data, they suffer from various drawbacks. One such challenge in satellite rainfall estimation is to detect rain and no-rain areas properly. To address this problem, in the present study we have used the Support Vector Machines (SVM). It is significant to note that this is the first study to report the utility of SVM in detecting rain and no-rain areas. The developed SVM based index performance has been evaluated by comparing with two most popular rain detection methods used for Indian regions i.e. Simple TIR threshold used in Global Precipitation Index (GPI) technique and Roca method used in Insat Multi Spectral Rainfall Algorithm (IMSRA). Performance of the above considered indices has been analyzed by considering various categorical statistics like Probabil ity of Detection (POD), Probability of no-rain detection (POND), Accuracy, Bias, False Alarm Ratio (FAR) and Heidke Skill Score (HSS). The obtained results clearly show that the new SVM based index performs much better than the earlier indices.
The main objective of this study is to validate and inter-compare two Near-Real-Time Satellite Ra... more The main objective of this study is to validate and inter-compare two Near-Real-Time Satellite Rainfall Estimates (NRT-SREs): INSAT Multispectral Rainfall Algorithm (IMSRA, simple blended product) and TMPA 3B42-RT V7 (3B42-RT, multisatellite product) across India. This study aims to provide some insight into the error characteristics of both the NRT-SREs to the algorithm developers and end users by inter-comparing the daily rainfall estimates during the southwest monsoon period of 2010–2013. This study utilizes various volu-metric statistics and categorical statistics to understand and evaluate the performance of NRT-SREs in terms of both spatial and volumetric error characteristics (hit, miss, and false error) at different rainfall thresholds across different Köppen–Geiger climate regions of India using the gridded gauge data provided by Indian Meteorological Department as reference dataset. A detailed statistical evaluation shows that the 3B42-RT performs comparatively better than the IMSRA across India. The results indicate that both IMSRA and 3B42-RT have a general tendency of overestimating the low rainfall rates (0– 2.5 mm/day) and underestimating the high rainfall rates (> 35.5 mm/day). At lower threshold values (0 and 2.5 mm/day), it is found that the miss error is dominant in IMSRA, whereas the false error is dominant in 3B42-RT. As the threshold increases (7.5 and 35.5 mm/day), both the miss and false errors increase in both SREs. Additionally, the spatial analysis of the results clearly indicate that the performance of the tested NRT-SREs is not uniform across different climatic regions, an important aspect to be considered for development/ improvement of the tested NRT-SRE algorithms.
Estimating accurate rainfall is very much needed for proper management of water resources. In cas... more Estimating accurate rainfall is very much needed for proper management of water resources. In case of scarcity in rain gauge stations as prevailing in most developing countries, accurate estimation becomes near impossible. To overcome this limitation, satellite data can be used which provides an alternate solution. However, there are still lots of challenges involved in satellite based rainfall estimations. One such challenge is to accurately detect the rain and no-rain pixels in a satellite image. As a solution, a new index i.e., TIRt<260K and TIRt -WVt<19K (Upadhyaya and Ramsankaran, 2014) has been developed for detecting rain and no-rain pixels in Kalpana-1 satellite images. This article presents the results of an evaluation study of the above mentioned index over Indian region. This study has been conducted for the south west monsoon season of the year 2013 using TRMM-2A25 rainfall rates as reference dataset. The newly developed index’s performance has been checked by comparing with two most popular rain detection methods used for Indian regions i.e. Simple TIR threshold used in Global Precipitation Index (GPI) technique and Roca method used in Insat Multi Spectral Rainfall Algorithm (IMSRA). Performance of the above considered indices has been analyzed by considering various categorical statistics like Probability of Detection (POD), Probability of no-rain detection (POND), Accuracy, Bias, False Alarm Ratio (FAR) and Heidke Skill Score (HSS). The obtained results clearly show that the new index performs much better than the earlier indices.
Rain is one of the major components of water cycle; extreme rain events can cause destruction and... more Rain is one of the major components of water cycle; extreme rain events can cause destruction and misery due to flash flood and droughts. Therefore, assessing rainfall at high temporal and spatial resolution is of fundamental importance which can be achieved only by satellite remote sensing. Conventional method like rain gauges is sparsely and unevenly distributed primarily over the land and with a coarse frequency of measurement. Whereas ground based radar is another method which is having high temporal but low spatial resolution and is considered to be too expensive for developing countries. Most of the satellite data is available free of cost which makes the rainfall estimation using satellite remote sensing data, a cost effective method. This paper reviews current and earlier techniques on rainfall estimation from satellite remote sensing observations.
Transportation Research Record Journal of the Transportation Research Board, Jan 11, 2015
ABSTRACT Gap acceptance predictions provide very important inputs for performance evaluation and ... more ABSTRACT Gap acceptance predictions provide very important inputs for performance evaluation and safety analysis of uncontrolled intersections and pedestrian midblock crossings. This paper deals with the application of Support Vector Machines (SVM), in understanding and classifying gaps at these facilities. SVMs are supervised learning technique originated from statistical learning theory and are widely used for classification and regression. The objective of this paper is to examine the feasibility of SVM in analyzing gap acceptance by comparing its results with existing statistical methods. To accomplish this objective, SVM and Binary Logit Models (BLM) were developed and compared using data collected at three different types of uncontrolled sections. SVM performance was found to be comparable with BLM in all cases and better in a few cases. Also the categorical statistics and skill scores used for validating the gap acceptance data revealed that SVM performs reasonably well. Thus the SVM technique can be used to classify and predict the accepted and rejected gap values based on speed and distance of the oncoming vehicles. This technique can be used in safety and advanced warning systems for the vehicles and pedestrians waiting to cross the main line stream vehicles.
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