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S. K. Gupta
and
S. N. Tiwari

Abstract

A simple algorithm has been developed for estimating the actual surface temperature by applying corrections to the effective brightness temperature measured by radiometers mounted on remote sensing platforms. Corrections to effective brightness temperature are computed using an accurate radiative transfer model for the “base atmosphere” and several modifications of this caused by deviations of the various atmospheric and surface parameters from their base model values. Model calculations are employed to establish simple analytical relations between the deviations of these parameters and the additional temperature corrections required to compensate for them. Effects of simultaneous variation of two parameters are also examined. Use of these analytical relations instead of detailed radiative transfer calculations for routine data analysis results in a severalfold reduction in computation costs.

Full access
A. K. Mitra
,
M. Das Gupta
,
S. V. Singh
, and
T. N. Krishnamurti

Abstract

A system for objectively producing daily large-scale analysis of rainfall for the Indian region has been developed and tested by using only available real-time rain gauge data and quantitative precipitation estimates from INSAT-1D IR data. The system uses a successive correction method to produce the analysis on a regular latitude–longitude grid. Quantitative precipitation estimates from the Indian National Satellite System (INSAT) operational geostationary satellite, INSAT-1D, IR data are used as the initial guess in the objective analysis method. Accumulated 24-h (daily) rainfall analyses are prepared each day by merging satellite and rain gauge data. The characteristics of the output from this analysis system have been examined by comparing the accumulated monthly observed rainfall with other available independent widely used datasets from the Global Precipitation Climatology Project (GPCP) and Climate Prediction Center Merged Analysis of Precipitation (CMAP) analyses. The monthly data prepared from the daily analyses are also compared with the subjectively analyzed India Meteorological Department (IMD) monthly rainfall maps. This comparison suggests that even with only the available real-time data from INSAT and rain gauge, it is possible to construct a usable large-scale rainfall map on regular latitude–longitude grids. This analysis, which uses a higher resolution and more local rain gauge data, is able to produce realistic details of the Indian summer monsoon rainfall patterns. The magnitude and distribution of orographic rainfall near the west coast of India is very different from and more realistic compared to both the GPCP and CMAP patterns. Due to the higher spatial resolution of the analysis system, the regions of heavy and light rain are demarcated clearly over the Indian landmass. Over the oceanic regions of the Arabian Sea, Bay of Bengal, and the equatorial Indian Ocean, the agreement of the analyzed rainfall at the monthly timescale is quite good compared to the other two datasets. For NWP and other model verification of large-scale rainfall, this dataset will be useful. In the field of rainfall monitoring within weather and climate research, this technique will have real-time applications with data from current (METSAT) and future (INSAT-3A and INSAT-3D) Indian geostationary satellites.

Full access
Kondapalli Niranjan Kumar
,
Ankur Gupta
,
T. S. Mohan
,
Akhilesh Kumar Mishra
,
Raghavendra Ashrit
,
Imranali M. Momin
,
Debasis K. Mahapatra
,
D. Nagarjuna Rao
,
Ashis K. Mitra
,
V. S. Prasad
, and
M. Rajeevan

Abstract

Drought, a prolonged natural event, profoundly impacts water resources and societies, particularly in agriculturally dependent nations like India. This study focuses on subseasonal droughts during the Indian summer monsoon season using standardized precipitation index (SPI). Analyzing hindcasts from the National Centre for Medium Range Weather Forecasting (NCMRWF) Extended Range Prediction (NERP) system spanning 1993–2015, we assess NERP’s strengths and limitations. NERP replicates climatic patterns well but overestimates rainfall in the Himalayan foothills and the Indo-Gangetic Plain while underestimating it in the core monsoon zone and western coastline. Nonetheless, the NERP system demonstrates its ability to predict subseasonal drought conditions across India. Our research explores the model’s dynamics, emphasizing tropical and extratropical influences. We evaluate the impact of monsoon intraseasonal oscillation (MSIO) and Madden–Julian oscillation (MJO) on drought onset and persistence, noting model performance and discrepancies. While the model consistently identifies MSIO locations, variations in phase propagation affect drought severity in India. Remarkably, NERP excels in predicting MJO phases during droughts. The study underscores the robust response in the near-equatorial Indian Ocean, a crucial factor in subseasonal drought development. Furthermore, we explored upper-level dynamic interactions, demonstrating NERP’s ability to capture subseasonal drought dynamics. For example, unusual westerly winds weaken the tropical easterly jet, and a cyclonic anomaly transports cold air at midlevels and upper levels. These interactions reduce thermal contrast, weakening monsoon flow and favoring drought conditions. Hence, the NERP system demonstrates its skill in assessing prevailing drought conditions and associated teleconnection patterns, enhancing our understanding of subseasonal droughts and their complex triggers.

Restricted access
Koray K. Yilmaz
,
Terri S. Hogue
,
Kuo-lin Hsu
,
Soroosh Sorooshian
,
Hoshin V. Gupta
, and
Thorsten Wagener

Abstract

This study compares mean areal precipitation (MAP) estimates derived from three sources: an operational rain gauge network (MAPG), a radar/gauge multisensor product (MAPX), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) satellite-based system (MAPS) for the time period from March 2000 to November 2003. The study area includes seven operational basins of varying size and location in the southeastern United States. The analysis indicates that agreements between the datasets vary considerably from basin to basin and also temporally within the basins. The analysis also includes evaluation of MAPS in comparison with MAPG for use in flow forecasting with a lumped hydrologic model [Sacramento Soil Moisture Accounting Model (SAC-SMA)]. The latter evaluation investigates two different parameter sets, the first obtained using manual calibration on historical MAPG, and the second obtained using automatic calibration on both MAPS and MAPG, but over a shorter time period (23 months). Results indicate that the overall performance of the model simulations using MAPS depends on both the bias in the precipitation estimates and the size of the basins, with poorer performance in basins of smaller size (large bias between MAPG and MAPS) and better performance in larger basins (less bias between MAPG and MAPS). When using MAPS, calibration of the parameters significantly improved the model performance.

Full access
S. Sorooshian
,
X. Gao
,
K. Hsu
,
R. A. Maddox
,
Y. Hong
,
H. V. Gupta
, and
B. Imam

Abstract

Recent progress in satellite remote-sensing techniques for precipitation estimation, along with more accurate tropical rainfall measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) instruments, have made it possible to monitor tropical rainfall diurnal patterns and their intensities from satellite information. One year (August 1998–July 1999) of tropical rainfall estimates from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) system were used to produce monthly means of rainfall diurnal cycles at hourly and 1° × 1° scales over a domain (30°S–30°N, 80°E–10°W) from the Americas across the Pacific Ocean to Australia and eastern Asia.

The results demonstrate pronounced diurnal variability of tropical rainfall intensity at synoptic and regional scales. Seasonal signals of diurnal rainfall are presented over the large domain of the tropical Pacific Ocean, especially over the ITCZ and South Pacific convergence zone (SPCZ) and neighboring continents. The regional patterns of tropical rainfall diurnal cycles are specified in the Amazon, Mexico, the Caribbean Sea, Calcutta, Bay of Bengal, Malaysia, and northern Australia. Limited validations for the results include comparisons of 1) the PERSIANN-derived diurnal cycle of rainfall at Rondonia, Brazil, with that derived from the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) radar data; 2) the PERSIANN diurnal cycle of rainfall over the western Pacific Ocean with that derived from the data of the optical rain gauges mounted on the TOGA-moored buoys; and 3) the monthly accumulations of rainfall samples from the orbital TMI and PR surface rainfall with the accumulations of concurrent PERSIANN estimates. These comparisons indicate that the PERSIANN-derived diurnal patterns at the selected resolutions produce estimates that are similar in magnitude and phase.

Full access
P. A. Francis
,
A. K. Jithin
,
J. B. Effy
,
A. Chatterjee
,
K. Chakraborty
,
A. Paul
,
B. Balaji
,
S. S. C. Shenoi
,
P. Biswamoy
,
A. Mukherjee
,
P. Singh
,
B. Deepsankar
,
S. Siva Reddy
,
P. N. Vinayachandran
,
M. S. Girish Kumar
,
T. V. S. Udaya Bhaskar
,
M. Ravichandran
,
A. S. Unnikrishnan
,
D. Shankar
,
A. Prakash
,
S. G. Aparna
,
R. Harikumar
,
K. Kaviyazhahu
,
K. Suprit
,
R. V. Shesu
,
N. Kiran Kumar
,
N. Srinivasa Rao
,
K. Annapurnaiah
,
R. Venkatesan
,
A. S. Rao
,
E. N. Rajagopal
,
V. S. Prasad
,
M. D. Gupta
,
T. M. Balakrishnan Nair
,
E. P. R. Rao
, and
B. V. Satyanarayana

Abstract

A good understanding of the general circulation features of the oceans, particularly of the coastal waters, and ability to predict the key oceanographic parameters with good accuracy and sufficient lead time are necessary for the safe conduct of maritime activities such as fishing, shipping, and offshore industries. Considering these requirements and buoyed by the advancements in the field of ocean modeling, data assimilation, and ocean observation networks along with the availability of the high-performance computational facility in India, Indian National Centre for Ocean Information Services has set up a “High-Resolution Operational Ocean Forecast and Reanalysis System” (HOOFS) with an aim to provide accurate ocean analysis and forecasts for the public, researchers, and other types of users like navigators and the Indian Coast Guard. Major components of HOOFS are (i) a suite of numerical ocean models configured for the Indian Ocean and the coastal waters using the Regional Ocean Modeling System (ROMS) for forecasting physical and biogeochemical state of the ocean and (ii) the data assimilation based on local ensemble transform Kalman filter that assimilates in situ and satellite observations in ROMS. Apart from the routine forecasts of key oceanographic parameters, a few important applications such as (i) Potential Fishing Zone forecasting system and (ii) Search and Rescue Aid Tool are also developed as part of the HOOFS project. The architecture of HOOFS, an account of the quality of ocean analysis and forecasts produced by it and important applications developed based on HOOFS are briefly discussed in this article.

Free access
P. A. Francis
,
A. K. Jithin
,
J. B. Effy
,
A. Chatterjee
,
K. Chakraborty
,
A. Paul
,
B. Balaji
,
S. S. C. Shenoi
,
P. Biswamoy
,
A. Mukherjee
,
P. Singh
,
B. Deepsankar
,
S. Siva Reddy
,
P. N. Vinayachandran
,
M. S. Girish Kumar
,
T. V. S. Udaya Bhaskar
,
M. Ravichandran
,
A. S. Unnikrishnan
,
D. Shankar
,
A. Prakash
,
S. G. Aparna
,
R. Harikumar
,
K. Kaviyazhahu
,
K. Suprit
,
R. V. Shesu
,
N. Kiran Kumar
,
N. Srinivasa Rao
,
K. Annapurnaiah
,
R. Venkatesan
,
A. S. Rao
,
E. N. Rajagopal
,
V. S. Prasad
,
M. D. Gupta
,
T. M. Balakrishnan Nair
,
E. P. R. Rao
, and
B. V. Satyanarayana
Full access
Thara Prabhakaran
,
P. Murugavel
,
Mahen Konwar
,
Neelam Malap
,
K. Gayatri
,
Shivsai Dixit
,
Soumya Samanta
,
Subharthi Chowdhuri
,
Sudarsan Bera
,
Mercy Varghese
,
J. Rao
,
J. Sandeep
,
P. D. Safai
,
A. K. Sahai
,
Duncan Axisa
,
A. Karipot
,
Darrel Baumgardner
,
Benjamin Werden
,
Ed Fortner
,
Kurt Hibert
,
Sathy Nair
,
Shivdas Bankar
,
Dinesh Gurnule
,
Kiran Todekar
,
Jerry Jose
,
V. Jayachandran
,
Pawan S. Soyam
,
Abhishek Gupta
,
Harish Choudhary
,
Aravindhavel
,
Suresh Babu Kantipudi
,
P. Pradeepkumar
,
R. Krishnan
,
K. Nandakumar
,
Peter F. DeCarlo
,
Doug Worsnop
,
G. S. Bhat
,
M. Rajeevan
, and
Ravi Nanjundiah

Abstract

The demand for effective methods to augment precipitation over arid regions of India has been increasing over the past several decades as the changing climate brings warmer average temperatures. In the fourth phase of the Cloud Aerosol Interaction and Precipitation Enhancement Experiment (CAIPEEX IV), a scientific investigation was conducted over a rain-shadow region of the Western Ghats mountains in India. The primary objective was to investigate the efficacy of hygroscopic seeding in convective clouds and to develop a cloud seeding protocol. CAIPEEX IV followed the World Meteorological Organization (WMO) recommendations in a peer-reviewed report with physical, statistical, and numerical investigations. The initial results of the campaign in the monsoon period of 2018 and 2019 with two instrumented aircraft, a ground-based dual-polarization C-band radar, a network of rain gauges, radiosondes, and surface aerosol measurements are reported here. The hygroscopic seeding material was detected in cloud droplets and key cloud microphysical processes in the seeding hypothesis were tracked. The formidable challenges of assessing seeding impacts in convective clouds and the results from 150 seed and 122 no-seed samples of randomized experiments are illustrated. Over 5,000 cloud passes from the airborne campaign provided details about the convective cloud properties as the key indicators for a seeding strategy and the evaluation protocol. The experimental results suggest that cloud seeding can be approached scientifically to reduce uncertainty. The results from this study should interest the scientific community and policymakers concerned with climate change’s impact on precipitation and how to mitigate rainfall deficiencies.

Open access
M. Ades
,
R. Adler
,
Rob Allan
,
R. P. Allan
,
J. Anderson
,
Anthony Argüez
,
C. Arosio
,
J. A. Augustine
,
C. Azorin-Molina
,
J. Barichivich
,
J. Barnes
,
H. E. Beck
,
Andreas Becker
,
Nicolas Bellouin
,
Angela Benedetti
,
David I. Berry
,
Stephen Blenkinsop
,
Olivier. Bock
,
Michael G. Bosilovich
,
Olivier. Boucher
,
S. A. Buehler
,
Laura. Carrea
,
Hanne H. Christiansen
,
F. Chouza
,
John R. Christy
,
E.-S. Chung
,
Melanie Coldewey-Egbers
,
Gil P. Compo
,
Owen R. Cooper
,
Curt Covey
,
A. Crotwell
,
Sean M. Davis
,
Elvira de Eyto
,
Richard A. M de Jeu
,
B.V. VanderSat
,
Curtis L. DeGasperi
,
Doug Degenstein
,
Larry Di Girolamo
,
Martin T. Dokulil
,
Markus G. Donat
,
Wouter A. Dorigo
,
Imke Durre
,
Geoff S. Dutton
,
G. Duveiller
,
James W. Elkins
,
Vitali E. Fioletov
,
Johannes Flemming
,
Michael J. Foster
,
Richard A. Frey
,
Stacey M. Frith
,
Lucien Froidevaux
,
J. Garforth
,
S. K. Gupta
,
Leopold Haimberger
,
Brad D. Hall
,
Ian Harris
,
Andrew K Heidinger
,
D. L. Hemming
,
Shu-peng (Ben) Ho
,
Daan Hubert
,
Dale F. Hurst
,
I. Hüser
,
Antje Inness
,
K. Isaksen
,
Viju John
,
Philip D. Jones
,
J. W. Kaiser
,
S. Kelly
,
S. Khaykin
,
R. Kidd
,
Hyungiun Kim
,
Z. Kipling
,
B. M. Kraemer
,
D. P. Kratz
,
R. S. La Fuente
,
Xin Lan
,
Kathleen O. Lantz
,
T. Leblanc
,
Bailing Li
,
Norman G Loeb
,
Craig S. Long
,
Diego Loyola
,
Wlodzimierz Marszelewski
,
B. Martens
,
Linda May
,
Michael Mayer
,
M. F. McCabe
,
Tim R. McVicar
,
Carl A. Mears
,
W. Paul Menzel
,
Christopher J. Merchant
,
Ben R. Miller
,
Diego G. Miralles
,
Stephen A. Montzka
,
Colin Morice
,
Jens Mühle
,
R. Myneni
,
Julien P. Nicolas
,
Jeannette Noetzli
,
Tim J. Osborn
,
T. Park
,
A. Pasik
,
Andrew M. Paterson
,
Mauri S. Pelto
,
S. Perkins-Kirkpatrick
,
G. Pétron
,
C. Phillips
,
Bernard Pinty
,
S. Po-Chedley
,
L. Polvani
,
W. Preimesberger
,
M. Pulkkanen
,
W. J. Randel
,
Samuel Rémy
,
L. Ricciardulli
,
A. D. Richardson
,
L. Rieger
,
David A. Robinson
,
Matthew Rodell
,
Karen H. Rosenlof
,
Chris Roth
,
A. Rozanov
,
James A. Rusak
,
O. Rusanovskaya
,
T. Rutishäuser
,
Ahira Sánchez-Lugo
,
P. Sawaengphokhai
,
T. Scanlon
,
Verena Schenzinger
,
S. Geoffey Schladow
,
R. W Schlegel
,
Eawag Schmid, Martin
,
H. B. Selkirk
,
S. Sharma
,
Lei Shi
,
S. V. Shimaraeva
,
E. A. Silow
,
Adrian J. Simmons
,
C. A. Smith
,
Sharon L Smith
,
B. J. Soden
,
Viktoria Sofieva
,
T. H. Sparks
,
Paul W. Stackhouse Jr.
,
Wolfgang Steinbrecht
,
Dimitri A. Streletskiy
,
G. Taha
,
Hagen Telg
,
S. J. Thackeray
,
M. A. Timofeyev
,
Kleareti Tourpali
,
Mari R. Tye
,
Ronald J. van der A
,
Robin, VanderSat B.V. van der Schalie
,
Gerard van der SchrierW. Paul
,
Guido R. van der Werf
,
Piet Verburg
,
Jean-Paul Vernier
,
Holger Vömel
,
Russell S. Vose
,
Ray Wang
,
Shohei G. Watanabe
,
Mark Weber
,
Gesa A. Weyhenmeyer
,
David Wiese
,
Anne C. Wilber
,
Jeanette D. Wild
,
Takmeng Wong
,
R. Iestyn Woolway
,
Xungang Yin
,
Lin Zhao
,
Guanguo Zhao
,
Xinjia Zhou
,
Jerry R. Ziemke
, and
Markus Ziese
Free access
Robert J. H. Dunn
,
F. Aldred
,
Nadine Gobron
,
John B. Miller
,
Kate M. Willett
,
M. Ades
,
Robert Adler
,
Richard, P. Allan
,
Rob Allan
,
J. Anderson
,
Anthony Argüez
,
C. Arosio
,
John A. Augustine
,
C. Azorin-Molina
,
J. Barichivich
,
H. E. Beck
,
Andreas Becker
,
Nicolas Bellouin
,
Angela Benedetti
,
David I. Berry
,
Stephen Blenkinsop
,
Olivier Bock
,
X. Bodin
,
Michael G. Bosilovich
,
Olivier Boucher
,
S. A. Buehler
,
B. Calmettes
,
Laura Carrea
,
Laura Castia
,
Hanne H. Christiansen
,
John R. Christy
,
E.-S. Chung
,
Melanie Coldewey-Egbers
,
Owen R. Cooper
,
Richard C. Cornes
,
Curt Covey
,
J.-F. Cretaux
,
M. Crotwell
,
Sean M. Davis
,
Richard A. M. de Jeu
,
Doug Degenstein
,
R. Delaloye
,
Larry Di Girolamo
,
Markus G. Donat
,
Wouter A. Dorigo
,
Imke Durre
,
Geoff S. Dutton
,
Gregory Duveiller
,
James W. Elkins
,
Vitali E. Fioletov
,
Johannes Flemming
,
Michael J. Foster
,
Stacey M. Frith
,
Lucien Froidevaux
,
J. Garforth
,
Matthew Gentry
,
S. K. Gupta
,
S. Hahn
,
Leopold Haimberger
,
Brad D. Hall
,
Ian Harris
,
D. L. Hemming
,
M. Hirschi
,
Shu-pen (Ben) Ho
,
F. Hrbacek
,
Daan Hubert
,
Dale F. Hurst
,
Antje Inness
,
K. Isaksen
,
Viju O. John
,
Philip D. Jones
,
Robert Junod
,
J. W. Kaiser
,
V. Kaufmann
,
A. Kellerer-Pirklbauer
,
Elizabeth C. Kent
,
R. Kidd
,
Hyungjun Kim
,
Z. Kipling
,
A. Koppa
,
B. M. Kraemer
,
D. P. Kratz
,
Xin Lan
,
Kathleen O. Lantz
,
D. Lavers
,
Norman G. Loeb
,
Diego Loyola
,
R. Madelon
,
Michael Mayer
,
M. F. McCabe
,
Tim R. McVicar
,
Carl A. Mears
,
Christopher J. Merchant
,
Diego G. Miralles
,
L. Moesinger
,
Stephen A. Montzka
,
Colin Morice
,
L. Mösinger
,
Jens Mühle
,
Julien P. Nicolas
,
Jeannette Noetzli
,
Ben Noll
,
J. O’Keefe
,
Tim J. Osborn
,
T. Park
,
A. J. Pasik
,
C. Pellet
,
Maury S. Pelto
,
S. E. Perkins-Kirkpatrick
,
G. Petron
,
Coda Phillips
,
S. Po-Chedley
,
L. Polvani
,
W. Preimesberger
,
D. G. Rains
,
W. J. Randel
,
Nick A. Rayner
,
Samuel Rémy
,
L. Ricciardulli
,
A. D. Richardson
,
David A. Robinson
,
Matthew Rodell
,
N. J. Rodríguez-Fernández
,
K.H. Rosenlof
,
C. Roth
,
A. Rozanov
,
T. Rutishäuser
,
Ahira Sánchez-Lugo
,
P. Sawaengphokhai
,
T. Scanlon
,
Verena Schenzinger
,
R. W. Schlegel
,
S. Sharma
,
Lei Shi
,
Adrian J. Simmons
,
Carolina Siso
,
Sharon L. Smith
,
B. J. Soden
,
Viktoria Sofieva
,
T. H. Sparks
,
Paul W. Stackhouse Jr.
,
Wolfgang Steinbrecht
,
Martin Stengel
,
Dimitri A. Streletskiy
,
Sunny Sun-Mack
,
P. Tans
,
S. J. Thackeray
,
E. Thibert
,
D. Tokuda
,
Kleareti Tourpali
,
Mari R. Tye
,
Ronald van der A
,
Robin van der Schalie
,
Gerard van der Schrier
,
M. van der Vliet
,
Guido R. van der Werf
,
A. Vance
,
Jean-Paul Vernier
,
Isaac J. Vimont
,
Holger Vömel
,
Russell S. Vose
,
Ray Wang
,
Markus Weber
,
David Wiese
,
Anne C. Wilber
,
Jeanette D. Wild
,
Takmeng Wong
,
R. Iestyn Woolway
,
Xinjia Zhou
,
Xungang Yin
,
Guangyu Zhao
,
Lin Zhao
,
Jerry R. Ziemke
,
Markus Ziese
, and
R. M. Zotta
Free access