Indian Institute of Technology Hyderabad (IITH) is a premier institute of science and technology established in 2008. IITH has been consistently ranked in the&nbsp;<a href="https://iith.ac.in/reports/" rel="noopener noreferrer">top 10 institutes</a>&nbsp;in India for Engineering&nbsp;<a href="https://www.nirfindia.org/2021/EngineeringRanking.html" rel="noopener noreferrer" target="_blank">according to NIRF</a>&nbsp;making it one of the most coveted schools for science and technology in the country.

IITH offers&nbsp;<a href="https://iith.ac.in/academics/under-graduate/">undergraduate programs</a>&nbsp;in all the classical engineering disciplines, applied sciences, design, as well as several modern interdisciplinary areas. Students are given a flexibility to explore a broad set of areas, and potentially pursue a minor or double major in a discipline that is not their own. Students who wish to seek a deeper understanding of their own discipline are strongly encouraged to get involved in cutting-edge research with the help of a faculty to mentor them, and earn an Honors in their own field.

The very foundation of IIT Hyderabad is based on&nbsp;<a href="https://iith.ac.in/research/">research and innovation</a>. The vibrant research culture is evident from the number of patents and publications that IITH produces consistently every year. IITH offers&nbsp;<a href="https://iith.ac.in/academics/programmes-offered/">graduate programs</a>&nbsp;at both a masters, and a doctoral level, in several diverse areas. There are separate programs for technology, design, science, and liberal arts. The&nbsp;<a href="https://iith.ac.in/academics/post-graduate/">MTech program</a>&nbsp;is offered in various modes and durations to cater to the ever-growing need of postgraduate level professionals in the country.

IITH encourages and supports&nbsp;<a href="https://iic-iith.netlify.app/index.html" rel="noopener noreferrer" target="_blank">innovation and entrepreneurship</a>&nbsp;at every stage. The minor program in entrepreneurship is open to all students of IITH, and many of the courses are offered by industrialists who are entrepreneurs themselves. There are various&nbsp;<a href="https://iith.ac.in/research/centres-incubators/">innovation centers and incubators</a>&nbsp;that promote entrepreneurship and help mentor young innovators. IITH has been very successful in building tie-ups with leading academic institutions around the globe.

IITH enjoys a very special relationship with Japanese universities and industries that goes beyond academic and research collaborations. In fact, some of the iconic buildings in IITH campus will carry the signature of Japanese architecture.

IITH creates a unique holistic ecosystem for education that offers interactive learning, a very flexible academic structure, cutting-edge research, strong industry collaboration, and entrepreneurship. This is an environment which enables students and faculty to translate their dreams into realities.

IIT Hyderabad

Temporal information from a geostationary (GEO) satellite is explored for improved precipitation characterization and quantification. Temporal predictors are extracted from the Geostationary Operational Environmental Satellite (GOES)-16 Advanced Baseline Imager (ABI). A 1-D convolutional neural network (CNN) architecture is proposed and compared to a deep neural network (DNN) benchmark without temporal predictors. While the CNN detection (rain/no-rain separation) is on par with the DNN benchmark, promising results are obtained for both precipitation type classification and quantification, indicating the value of the temporal information for precipitation estimation. The identification of rapidly evolving convective systems is improved (accuracy improved from 49% to 58%), while false detections in stratiform-type precipitation are reduced. Rain rate quantification is improved by reducing the overestimation of low rain rates, which increases the correlation from 0.34 (DNN) to 0.41 (CNN). This study is an important first step toward better characterizing and quantifying precipitation using the very high temporal resolution of GEO satellites. © 2004-2012 IEEE.

Department of Civil Engineering

World University root node

IEEE Geoscience and Remote Sensing Letters

Exploring the Temporal Information From GEO Satellites for Estimating Precipitation With Convolutional Neural Networks

Fulltext

Frontiers in Climate

Editorial: A quest to fully understand precipitation: Novel methods to characterize, model, and detect precipitation processes

The study introduces a new quantitative precipitation estimation (QPE) algorithm from Advanced Baseline Imager (ABI) observations from GOES-16 across the contiguous USA (CONUS). It is developed and comprehensively evaluated using the Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) system as a benchmark, and features Random Forest (RF) machine learning-based QPE. The key innovations of the algorithm include a comprehensive set of satellite predictors derived from five infrared ABI channels, complemented by low-level environmental conditions from the Rapid Refresh (RAP) numerical weather prediction (NWP) model, and outputs of probability of precipitation type for seamless integration of varying precipitation rates across types. A systematic analysis of the predictors is performed. The analysis reveals that satellite predictors contribute more to high-intensity precipitation estimates, whereas environmental predictors primarily condition low-intensity precipitation. Combining both categories of predictors improves scores (correlation coefficient, CC = 0.41) overall, with the greatest improvement in the warm stratiform precipitation type. Introducing precipitation type information through probabilities further improves correlation (0.46). An intercomparison of the RF model with the Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) shows that RF has better detection and quantification scores than SCaMPR (Heidke Skill Score, HSS = 0.91 vs. 0.19; CC = 0.44 vs. 0.26). Both retrievals display similar performance patterns across different regions of the CONUS. For example, skill degrades in complex terrain. Precipitation processes in complex terrain and their variability may not be well captured, especially with the degraded ABI resolution in the western CONUS. It is recommended to complement the 11.2 μm legacy channel with at least the 6.2 μm channel for global precipitation retrievals using GEO sensors. © 2022 Royal Meteorological Society.

Quarterly Journal of the Royal Meteorological Society

Towards improved precipitation estimation with the GOES-16 advanced baseline imager: Algorithm and evaluation

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 have the greatest 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 and stratiform) with multispectral 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 as a benchmark. The developed prognostic model shows skillful performance in identifying the occurrence/nonoccurrence of precipitation (accuracy of 97%; Kappa coefficient of 0.9) and precipitation processes, with 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. © 2021 Royal Meteorological Society

Classifying precipitation from GEO satellite observations: Prognostic model

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 on board 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 850–500 hPa 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 vapour channel T6.2 with the infrared 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. © 2021 Royal Meteorological Society

Classifying precipitation from GEO satellite observations: Diagnostic model

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. © 2020 American Meteorological Society.

Journal of Hydrometeorology

On the propagation of satellite precipitation estimation errors: From passive microwave to infrared estimates

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. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

International Journal of Remote Sensing

Investigating the performance of satellite and reanalysis rainfall products at monthly timescales across different rainfall regimes of Ethiopia

Considering the importance of the error estimates for satellite rainfall products in various applications, the present article deals with the development of an Error Model for Modified-INSAT Multi- Spectral Rainfall Algorithm (M-IMSRA) Estimates (EMME), a recently developed climate region scale rainfall algorithm across India. A non-parametric framework has been adopted to model all the four error components: Correct No-Rain Detection, Miss Rain, False Rain, and Hit Rain in M-IMSRA estimates at climate region scale. The developed error model generated convincing realization of reference rainfall for the estimated rainfall from M-IMSRA algorithm across all the climate regions of India. Exceptions are the high intensity hit rain events across arid Thar Desert and arid Himalayan regions and miss rain events across arid Himalayan region. Overall, the developed error model showed promising results in modelling hit, miss, and false error components of daily M-IMSRA estimates and thus can be associated with the M-IMSRA estimates. © 2018 Informa UK Limited, trading as Taylor & Francis Group. All Rights Reserved.

Error modelling for modified-INSAT multi-spectral rainfall algorithm

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 volumetric 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. © 2017, Springer International Publishing AG, part of Springer Nature.

Environmental Monitoring and Assessment

Comprehensive inter-comparison of INSAT multispectral rainfall algorithm estimates and TMPA 3B42-RT V7 estimates across different climate regions of India during southwest monsoon period

Drought is a recurrent phenomenon in the arid and semi-arid regions of Ethiopia. In the past two decades, millions of people are affected by drought throughout the country. To assess and monitor droughts, accurate rainfall record is crucial at different spatial and temporal scales. However, rain gauges are sparsely distributed across Ethiopia. To overcome this limitation, considering the high spatial resolution and longer period record of the Climate Hazard group Infrared Precipitation with Stations (CHIRPS) v2 satellite rainfall data, the same has been used as an input to Effective drought index (EDI) for drought monitoring in Ethiopia . The study has been carried out across the three different rainfall regimes of Ethiopia for the period from 1981 to 2016. The obtained results show that the historical drought years across rainfall regime of Ethiopia are well captured by EDI with CHIRPS rainfall product as input variable. Thus, highlighting the efficacy of EDI index and CHIRPS rainfall product for operational drought monitoring in Ethiopia. © 2017 ACRS. All rights reserved.

38th Asian Conference on Remote Sensing - Space Applications: Touching Human Lives, ACRS 2017

Meteorological drought monitoring across different rainfall regimes of Ethiopia using chirps V2- Rainfall data

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. © 2016

Remote Sensing of Environment

Modified-INSAT Multi-Spectral Rainfall Algorithm (M-IMSRA) at climate region scale: Development and validation

Gap acceptance predictions provide very important inputs for performance evaluation and safety analysis of uncontrolled intersections and pedestrian midblock crossings. The focus of this paper Ls on the application of support vector machines (SVMs) in understanding and classifying gaps at these facilities. The SVMs are supervised learning techniques originating from statistical learning theory and are widely used for classification and regression. In this paper, the feasibility of the SVM in analyzing gap acceptance Ls examined by comparing its results with existing statistical methods. To accomplish that objective, SVM and binary logit models (BLMs) were developed and compared by using data collected at three type.s of uncontrolled Intersections. SVM performance was found to be comparable with that of the BLM in all cases and better in a few. ALso, the categorical statistics and skill scores used for validating gap acceptance data revealed that the SVM performed reasonably well. Thus, the SVM technique can be used to classify and predict accepted and rejected gap values according to speed and distance of oncoming vehicles. This technique can be used in advance safety warning systems for vehicles and pedestrians waiting to cross major stream vehicles.

Transportation Research Record

Classification of gaps at uncontrolled intersections and midblock crossings using support vector machines

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 (TIRt &lt; 260K and TIRt - WVt &lt; 19 K, where TIRt and WVt 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 × 5 pixels at time t (mean5)] 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. © 2014 American Meteorological Society.