A New Technique to Estimate Precipitation at Fine Scale Using Multifrequency Satellite Observations Over Indian Land and Oceanic Regions

Meteorological Applications

2018

Self Cite

Clouds are important in a climate system due to their impact on the radiation budget and precipitation. Changes in cloud cover are hard to infer due to lack of reliable long‐term cloud datasets. An attempt is made in this study to investigate the changes in cloud cover using the relationship between precipitation extremes and clouds. Heavy precipitation is associated with convective clouds while light precipitation occurs mostly with low clouds. The Global Precipitation and Climatology Project (GPCP) precipitation data are used in this study to relate the changes in heavy and light precipitation with those in convective and low cloud cover, respectively, from the Visible and Infrared Scanner data of the Tropical Rainfall Measuring Mission available from 1998 to 2014. Slopes were derived between changes in precipitation extremes and cloud cover using monthly data. These slopes were applied to long‐term trends of precipitation extremes from GPCP data (1979–2016) to infer long‐term changes in convective and low cloud cover. Cloud cover derived using this technique shows substantial inter‐monthly and inter‐annual variability. The results show an increase of about 4.48 ± 1.9% per decade in convective cloud cover over tropical ocean (25 ° S–25 ° N). This is consistent with National Oceanic and Atmospheric Administration (NOAA) High Resolution Infrared Radiometer Sounder (HIRS) observations, which show an increase of about 5.04 ± 2.18% per decade in convective cloud cover over tropical ocean. In the present study an increasing trend of about 5.54 ± 2.07% in convective cloud cover over land (20 °–60 ° N) is also derived, which is comparable to the NOAA HIRS trend of about 6.57 ± 2.53% increase per decade. Decreases of about 3.52 ± 1.69% and 4.26 ± 1.48% per decade in low cloud cover over tropical ocean and northern mid‐latitude land, respectively, are reported and are consistent with decreases of about 3.05 ± 1.68% and 5.31 ± 2.22% from NOAA HIRS data over those regions.

Section: Convective Clouds Over Tropical Oceansmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigating changes in cloud cover using the long‐term record of precipitation extremes

Meteorological Applications

2018

Self Cite

“…These heavy precipitation events cause flood-like disasters. Recently, various parts of the Indian region experienced flood-like events [26][27][28]. The present technique can be very useful for the monitoring of near-real-time flash flood events, which can be helpful for mitigation and adaptation actions against flood-related disasters.…”

Section: Discussionmentioning

confidence: 97%

Remote Sensing of Near-Real-Time Heavy Precipitation Using Observations from GPM and MFG over India and Nearby Oceanic Regions

Rafiq

The 2nd International Electronic Conference on Remote Sensing

2018

Self Cite

This study deals with the integration of merging highly accurate precipitation estimates from the Global Precipitation Measurement (GPM) with sampling gap-free satellite observations from Meteosat 7 of the Meteosat First Generation (MFG) to develop a regional rainfall monitoring algorithm to monitor precipitation over India and nearby oceanic regions. For this purpose, we derived precipitation signatures from Meteosat observations to co-locate them against precipitation from GPM. A relationship was then established between rainfall and rainfall signature using observations from various rainy seasons. The relationship thus derived can be used to monitor precipitation over India and nearby oceanic regions. The performance of this technique was tested against rain gauges and global precipitation products including the Global Satellite Mapping of Precipitation (GSMaP), Climate Prediction Centre MORPHing (CMORPH), Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) and Integrated Multi-satellitE Retrievals for GPM (IMERG). A case study is presented here to examine the performance of the developed algorithm in monitoring heavy rainfall during the flood event in Tamil Nadu in 2015. This is the first attempt to use near-real-time observations from GPM and MFG to monitor heavy precipitation over the Indian region. Due to its finer resolution and near-real-time availability, this technique can be used to monitor near-real-time flash floods.

“…Persistent high levels of precipitable water over the region resulted in multi‐day heavy precipitation episodes during November and the first week of December in 2015, ultimately leading to severe flooding. Mishra () monitored the Chennai flood of 2015 using a satellite‐based remote sensing regional rainfall product developed by the same author in an earlier study (Mishra, ).…”

Section: Introductionmentioning

confidence: 99%

An observational study of the Kanchipuram flood during the northeast monsoon season in 2015

Chandra

Rafiq

et al. 2018

Weather

Southeast India experienced several extreme precipitation events during November and early December 2015, which resulted in heavy flooding over Tamilnadu and Puducherry. Kanchipuram was one of the worst affected districts of Tamilnadu. The devastating floods that hit Kanchipuram during November/December 2015 caused serious damage to property and loss of human life. This study focuses on rain gauge data from 13 stations in Kanchipuram. The results show that there were multiple heavy precipitation events during the second week of November and the first week of December that resulted in record‐breaking heavy runoff over Kanchipuram, causing severe flooding over the region. The extremely heavy precipitation over Kanchipuram occurred in multiple spells: 7–9 November, 15–17 November and 1/2 December 2015. Tambram station recorded a maximum rainfall value of about 494 mm on 2 December. Peak rainfall amounts of about 329 and 316 mm were also recorded over Tambram and Panankattucheri on 16 and 9 November, respectively. It was found that multi‐day heavy precipitation events during November and December caused heavy flooding over Kanchipuram.