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Journal ArticleDOI

Performance Analysis of IMD High-Resolution Gridded Rainfall (0.25° × 0.25°) and Satellite Estimates for Detecting Cloudburst Events over the Northwest Himalayas

01 Jul 2020-Journal of Hydrometeorology (American Meteorological Society)-Vol. 21, Iss: 7, pp 1549-1569
TL;DR: The presence of a sparse rain gauge network in complex terrain like Himalaya has encouraged the present study for the concerned evaluation of Indian Meteorological Department (IMD) ground-b... as discussed by the authors.
Abstract: The presence of a sparse rain gauge network in complex terrain like Himalaya has encouraged the present study for the concerned evaluation of Indian Meteorological Department (IMD) ground-b...

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Citations
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01 Dec 2017
TL;DR: A high-resolution bias-corrected precipitation and temperature data that can be used to monitor near real-time drought conditions over South Asia and can effectively capture observed drought conditions as shown by the satellite-based drought estimates.
Abstract: Drought in South Asia affect food and water security and pose challenges for millions of people. For policy-making, planning, and management of water resources at sub-basin or administrative levels, high-resolution datasets of precipitation and air temperature are required in near-real time. We develop a high-resolution (0.05°) bias-corrected precipitation and temperature data that can be used to monitor near real-time drought conditions over South Asia. Moreover, the dataset can be used to monitor climatic extremes (heat and cold waves, dry and wet anomalies) in South Asia. A distribution mapping method was applied to correct bias in precipitation and air temperature, which performed well compared to the other bias correction method based on linear scaling. Bias-corrected precipitation and temperature data were used to estimate Standardized precipitation index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) to assess the historical and current drought conditions in South Asia. We evaluated drought severity and extent against the satellite-based Normalized Difference Vegetation Index (NDVI) anomalies and satellite-driven Drought Severity Index (DSI) at 0.05°. The bias-corrected high-resolution data can effectively capture observed drought conditions as shown by the satellite-based drought estimates. High resolution near real-time dataset can provide valuable information for decision-making at district and sub-basin levels. Machine-accessible metadata file describing the reported data (ISA-Tab format)

104 citations

Journal ArticleDOI
TL;DR: In this article, a fine-scaled ground validation of eleven Gridded Precipitation Products (GPPs) including five satellite-based (GPM-IMERGV06, TRMM-3B42RTV7, TSRV7RT, CHIRPS-2.0 and PERSIANN-CCS) and two gauge-interpolated (IMD-0.25° and APHRODITE-2V18) GPPs in Eastern Himalaya.

24 citations

Journal ArticleDOI
TL;DR: In this article , a comparative analysis of twelve gridded rainfall datasets in representing the spatial and temporal variation of extreme rainfall events and trends in the recent past (1983-2015) in India is presented.

14 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a comparative analysis of twelve gridded rainfall datasets in representing the spatial and temporal variation of extreme rainfall events and trends in the recent past (1983-2015) in India.

14 citations

Journal ArticleDOI
01 Jun 2022
TL;DR: In this paper , the authors examined the role of antecedent soil moisture on floods caused by tropical cyclones and found that the distribution of extreme rainfall over a basin from TCs depend on the translation speed, track length, and size of the TC.
Abstract: Landfalling tropical cyclones (TCs) cause severe damage to infrastructure and economic losses in India. Extreme rainfall from TCs can lead to flooding, which disrupts the lives of people and their socio-economic well-being. Despite the severe hazards caused by TCs in India, their impacts on river basin-scale flooding are not well examined. Here, we use cyclone tracks from India Meteorological Department (IMD) and ERA5-Land reanalysis to identify the drivers of TC induced floods in India. We select TCs that affected the Subarnarekha, Brahmani, Mahanadi, and Vamshadhara river basins during the 1981–2019 period. We estimated return periods of extreme daily and hourly rainfall and total runoff associated with TCs. We examined the role of antecedent soil moisture on floods caused by TCs. Our results show that the distribution of extreme rainfall over a basin from TCs depend on the translation speed, track length, and size of the TC. Severe flooding in the basins is strongly linked to antecedent soil moisture conditions. Regardless of extreme rainfall, flooding due to TCs during the pre-monsoon (April–May) is less likely because of dry antecedent soil moisture conditions. On the other hand, TCs can lead to severe flooding during the summer monsoon (June–September) period due to relatively wetter antecedent conditions. In the post-monsoon season (October–November), the severity of flooding caused by TCs is higher than the pre-monsoon season, and as we move further into the post-monsoon this severity reduces. Our findings highlight the need to monitor the land-surface characteristics and TC track prediction to identify the potential of flooding from landfalling TCs.

4 citations

References
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Book
03 Jun 2011
TL;DR: The second edition of "Statistical Methods in the Atmospheric Sciences, Second Edition" as mentioned in this paper presents and explains techniques used in atmospheric data summarization, analysis, testing, and forecasting.
Abstract: Praise for the First Edition: 'I recommend this book, without hesitation, as either a reference or course text...Wilks' excellent book provides a thorough base in applied statistical methods for atmospheric sciences' - "BAMS" ("Bulletin of the American Meteorological Society"). Fundamentally, statistics is concerned with managing data and making inferences and forecasts in the face of uncertainty. It should not be surprising, therefore, that statistical methods have a key role to play in the atmospheric sciences. It is the uncertainty in atmospheric behavior that continues to move research forward and drive innovations in atmospheric modeling and prediction. This revised and expanded text explains the latest statistical methods that are being used to describe, analyze, test and forecast atmospheric data. It features numerous worked examples, illustrations, equations, and exercises with separate solutions. "Statistical Methods in the Atmospheric Sciences, Second Edition" will help advanced students and professionals understand and communicate what their data sets have to say, and make sense of the scientific literature in meteorology, climatology, and related disciplines. This book presents and explains techniques used in atmospheric data summarization, analysis, testing, and forecasting. Chapters feature numerous worked examples and exercises. Model Output Statistic (MOS) includes an introduction to the Kalman filter, an approach that tolerates frequent model changes. It includes a detailed section on forecast verification, including statistical inference, diagrams, and other methods. It provides an expanded treatment of resampling tests within nonparametric tests. It offers an updated treatment of ensemble forecasting. It provides expanded coverage of key analysis techniques, such as principle component analysis, canonical correlation analysis, discriminant analysis, and cluster analysis. It includes careful updates and edits throughout, based on users' feedback.

6,768 citations

Journal ArticleDOI
TL;DR: The TRMM Multi-Satellite Precipitation Analysis (TMPA) as discussed by the authors provides a calibration-based sequential scheme for combining precipitation estimates from multiple satellites, as well as gauge analyses where feasible, at fine scales.
Abstract: The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) provides a calibration-based sequential scheme for combining precipitation estimates from multiple satellites, as well as gauge analyses where feasible, at fine scales (0.25° × 0.25° and 3 hourly). TMPA is available both after and in real time, based on calibration by the TRMM Combined Instrument and TRMM Microwave Imager precipitation products, respectively. Only the after-real-time product incorporates gauge data at the present. The dataset covers the latitude band 50°N–S for the period from 1998 to the delayed present. Early validation results are as follows: the TMPA provides reasonable performance at monthly scales, although it is shown to have precipitation rate–dependent low bias due to lack of sensitivity to low precipitation rates over ocean in one of the input products [based on Advanced Microwave Sounding Unit-B (AMSU-B)]. At finer scales the TMPA is successful at approximately reproducing the s...

6,179 citations

Proceedings ArticleDOI
01 Jan 1968
TL;DR: In many fields using empirical areal data there arises a need for interpolating from irregularly-spaced data to produce a continuous surface as discussed by the authors, and it is assumed that a unique number (such as rainfall in meteorology, or altitude in geography) is associated with each data point.
Abstract: In many fields using empirical areal data there arises a need for interpolating from irregularly-spaced data to produce a continuous surface. These irregularly-spaced locations, hence referred to as “data points,” may have diverse meanings: in meterology, weather observation stations; in geography, surveyed locations; in city and regional planning, centers of data-collection zones; in biology, observation locations. It is assumed that a unique number (such as rainfall in meteorology, or altitude in geography) is associated with each data point. In order to display these data in some type of contour map or perspective view, to compare them with data for the same region based on other data points, or to analyze them for extremes, gradients, or other purposes, it is extremely useful, if not essential, to define a continuous function fitting the given values exactly. Interpolated values over a fine grid may then be evaluated. In using such a function it is assumed that the original data are without error, or that compensation for error will be made after interpolation.

3,882 citations

Journal ArticleDOI
TL;DR: The Variable Infiltration Capacity model, a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights, is presented and it is shown that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.
Abstract: The Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset builds on previous approaches to ‘smart’ interpolation techniques and high resolution, long period of record precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. The algorithm i) is built around a 0.05° climatology that incorporates satellite information to represent sparsely gauged locations, ii) incorporates daily, pentadal, and monthly 1981-present 0.05° CCD-based precipitation estimates, iii) blends station data to produce a preliminary information product with a latency of about 2 days and a final product with an average latency of about 3 weeks, and iv) uses a novel blending procedure incorporating the spatial correlation structure of CCD-estimates to assign interpolation weights. We present the CHIRPS algorithm, global and regional validation results, and show how CHIRPS can be used to quantify the hydrologic impacts of decreasing precipitation and rising air temperatures in the Greater Horn of Africa. Using the Variable Infiltration Capacity model, we show that CHIRPS can support effective hydrologic forecasts and trend analyses in southeastern Ethiopia.

2,895 citations

Journal ArticleDOI
TL;DR: In this article, the shape and intensity of the precipitation features are modified during the time between microwave sensor scans by performing a time-weighted linear interpolation, yielding spatially and temporally complete microwave-derived precipitation analyses, independent of the infrared temperature field.
Abstract: A new technique is presented in which half-hourly global precipitation estimates derived from passive microwave satellite scans are propagated by motion vectors derived from geostationary satellite infrared data. The Climate Prediction Center morphing method (CMORPH) uses motion vectors derived from half-hourly interval geostationary satellite IR imagery to propagate the relatively high quality precipitation estimates derived from passive microwave data. In addition, the shape and intensity of the precipitation features are modified (morphed) during the time between microwave sensor scans by performing a time-weighted linear interpolation. This process yields spatially and temporally complete microwave-derived precipitation analyses, independent of the infrared temperature field. CMORPH showed substantial improvements over both simple averaging of the microwave estimates and over techniques that blend microwave and infrared information but that derive estimates of precipitation from infrared data...

2,784 citations