Assessing the performance of multi-sources gridded data to estimate long-term rainfall change over north-central region of India

As station data quality and availability is not adequate to reliably estimate observed climate change over many parts of the country, multi sources observational gridded datasets have been employed in the present study. The performances of multi-observational gridded datasets, e.g., IMD gridded data, CRU, APHRODITE, GPCC, NCAR/NCEP reanalysis have been compared with the reference rainfall data from IITM over North central India (NCI), a region of subtropical monsoon climate, during four main seasons (MAM,JJAS,ON and DJF) as well as in annual scale for the period 1951-2003. All the gridded data except CRU and NCEP have secured good skill scores in all seasons as well as at annual scale. APHRODITE and NCEP reanalysis have shown large wet bias in all seasons. The reference rainfall data over NCI has shown 6.3 mm, 4.2 mm, 1.9 mm and 11.2 mm increase per decade for MAM, JJAS, DJF seasons and annual rainfall respectively whereas 2.2 mm decrease per decade has been found for ON season. Only GPCC dataset have been able to capture similar trend for all seasons. Performance of NCEP reanalysis has been worse in compared to others. GPCC and IMD high resolution data has shown smallest bias among all the datasets and also obtain superior skill scores than others. Therefore based on visual inspection and the results from different conventional measures, GPCC high resolution gridded data and high resolution IMD gridded data may be reliably used for climatic analysis of this region.

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The Regionalization of Indonesian Maritime Continent Rainfall based on Integrated Multi-satellite Retrievals for GPM (IMERG)

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/893/1/012065 ◽

2021 ◽

Vol 893 (1) ◽

pp. 012065

Author(s):

IWA Yuda ◽

T Osawa ◽

M Nagai ◽

R Prasetia

Keyword(s):

Correlation Method ◽

Rainfall Data ◽

Annual Rainfall ◽

North Central ◽

Rain Gauges ◽

The North ◽

Satellite Retrievals ◽

North Part ◽

The Republic ◽

Observation Tools

Abstract The need for adequate rainfall data in all regions of Indonesia cannot be achieved only by relying on ground observation tools. This work aims to evaluate the application of spatial satellite rainfall data in characterizing rainfall associated with climatic condition over Indonesia. This study applied an Integrated Multi-satellite Retrievals for GPM (IMERG) data using a double correlation method (DCM). The analysis was carried out in the period April 2014 to March 2019. Before regionalization, IMERG V06 data were validated using observed rainfall data from the Agency for Meteorology, Climatology, and Geophysics of the Republic of Indonesia (BMKG). The results showed that 96% of 154 total validation locations have a high correlation score between IMERG and rain gauges (r = 0.5 – 0.97). IMERG was also able to identify monthly and annual rainfall patterns in Indonesia. Based on DCM, we obtained four rainfall regions in Indonesia. Region A has the monsoonal characteristic, covers central and south Indonesia from south Sumatra to Nusa Tenggara, south parts of Kalimantan, some areas of Sulawesi, and parts of Papua. Region B has an equatorial pattern (semi-monsoonal), located in the equatorial area of Indonesia and covers the west and east part of Sumatra and the north-central part of Kalimantan. Region C, with an anti-monsoonal pattern, covers Maluku, western-central Papua, and parts of Sulawesi. Region D is influenced by monsoon and cold surge characteristics, located in the north part of Sumatera and a small portion of northern Kalimantan to the South China Sea region. Besides the new region D, this research also showed five other differences between IMERG-based map and gridded rain gauges’ data-based map (2003). The regionalization results based on IMERG reveal that there is a possibility of updating areas with certain rainfall characters in Indonesia related to resolution, density, and updates data sources.

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A rationale for using local and regional point rainfall data for design and analysis of urban storm drainage systems

Water Science & Technology ◽

10.2166/wst.1998.0423 ◽

1998 ◽

Vol 37 (11) ◽

pp. 7-14 ◽

Cited By ~ 5

Author(s):

P. S. Mikkelsen ◽

H. Madsen ◽

K. Arnbjerg-Nielsen ◽

H. K. Jørgensen ◽

D. Rosbjerg ◽

...

Keyword(s):

Time Series ◽

High Resolution ◽

Regional Distribution ◽

Regional Analysis ◽

Rainfall Data ◽

Annual Rainfall ◽

Small Scale ◽

Sampling Errors ◽

Actual Length ◽

Geographical Variations

The Danish measuring network for high-resolution rainfall data consists of approximately 70 tipping bucket rain gauges of which 41 have been operated for more than 10 years. The gauges are separated by one to 300 km and cover an area of 43,000 km2. Significant geographical variations of extreme rainfall characteristics have been observed. Part of these variations can be explained by correlation with the mean annual rainfall and the existence of a metropolitan effect in the Greater Copenhagen area. The remaining variation may be attributed to sampling errors and small-scale spatial variations close to the gauges. Engineering methodologies all require rain data of some kind, ranging from design storms based on idf-curves for use in simple calculations to high-resolution time series for use in detailed simulation studies. A comprehensive regional analysis was carried out to account for the geographical variation and to improve estimation for large return periods exceeding the actual length of the measured time series. Ideally, rainfall data used as input to urban drainage calculations should always be based on regional rain information. Regional design storms can be made readily available based on theory developed in this study. However, a satisfactory framework for generating synthetic rain series from regional rain information is not yet available. Thus, there will still be a need for using historical rain series in the near future. To improve the basis for choosing representative historical rain series all the Danish gauges have been classified according to their deviations from the regional distribution.

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NEW VIEWS ON AN OLD LANDSCAPE: EMPLOYING AN UNMANNED AIRCRAFT SYSTEM (UAS) FOR RAPID HIGH RESOLUTION DATA ACQUISITION TO AID GEOMORPHOLOGICAL RESEARCH

10.1130/abs/2017am-305704 ◽

2017 ◽

Author(s):

Phillip P. Allen ◽

◽

Matthew E. Ramspott

Keyword(s):

High Resolution ◽

Data Acquisition ◽

Unmanned Aircraft ◽

Unmanned Aircraft System ◽

High Resolution Data ◽

Aircraft System ◽

Resolution Data

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Overall and Partial Trend Identification of High Resolution Gridded Rainfall Data over Vietnam by Applying Innovative-Şen Trend Analysis (ITA) Method

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/652/1/012022 ◽

2021 ◽

Vol 652 (1) ◽

pp. 012022

Author(s):

T H Phan ◽

N D P Dang ◽

M H Le ◽

T H Nguyen ◽

N T Nguyen ◽

...

Keyword(s):

High Resolution ◽

Trend Analysis ◽

Rainfall Data ◽

Trend Identification

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High-Resolution Traffic Sensing with Probe Autonomous Vehicles: A Data-Driven Approach

Sensors ◽

10.3390/s21020464 ◽

2021 ◽

Vol 21 (2) ◽

pp. 464

Author(s):

Wei Ma ◽

Sean Qian

Keyword(s):

High Resolution ◽

Autonomous Vehicles ◽

Penetration Rate ◽

Data Driven ◽

Traffic Information ◽

Flow Density ◽

Massive Data ◽

Market Penetration ◽

High Resolution Data ◽

Data Driven Approach

Recent decades have witnessed the breakthrough of autonomous vehicles (AVs), and the sensing capabilities of AVs have been dramatically improved. Various sensors installed on AVs will be collecting massive data and perceiving the surrounding traffic continuously. In fact, a fleet of AVs can serve as floating (or probe) sensors, which can be utilized to infer traffic information while cruising around the roadway networks. Unlike conventional traffic sensing methods relying on fixed location sensors or moving sensors that acquire only the information of their carrying vehicle, this paper leverages data from AVs carrying sensors for not only the information of the AVs, but also the characteristics of the surrounding traffic. A high-resolution data-driven traffic sensing framework is proposed, which estimates the fundamental traffic state characteristics, namely, flow, density and speed in high spatio-temporal resolutions and of each lane on a general road, and it is developed under different levels of AV perception capabilities and for any AV market penetration rate. Experimental results show that the proposed method achieves high accuracy even with a low AV market penetration rate. This study would help policymakers and private sectors (e.g., Waymo) to understand the values of massive data collected by AVs in traffic operation and management.

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