Improvement of TMI Rain Retrieval Over the Indian Subcontinent

Shige, Shoichi; Yamamoto, Munehisa K.; Taniguchi, Aina

doi:10.1002/9781118872086.ch2

Cited by 21 publications

(13 citation statements)

References 67 publications

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“…The GSMaP algorithm combines the information from microwave and infrared radiometers aboard multiple satellites [53]. The algorithmic structure of the GSMaP products was discussed in detail in the literature [16,[54][55][56]. GSMaP products are offered at two spatial resolutions: 0.1 • × 0.1 • and 0.25 • × 0.25 • grids, and two temporal scales: hourly and daily.…”

Section: Satellite-based and Rain Gauge-based Precipitation Datasetsmentioning

confidence: 99%

Evaluation and Bias Correction of Satellite-Based Rainfall Estimates for Modelling Flash Floods over the Mediterranean region: Application to Karpuz River Basin, Turkey

Saber

Yılmaz

2018

Water

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This study investigates the utility of satellite-based rainfall estimates in simulating flash floods in Karpuz River Basin, Turkey, characterized by limited rain gauge network. Global Satellite Mapping of Precipitation (GSMaP) product was evaluated with the rain gauge network at daily and monthly timescales considering seasonality, elevation zones, extreme events and rainfall intensity thresholds. Statistical analysis indicated that GSMaP shows acceptable linear correlation coefficient with rain gauges, however, suffers from significant underestimation bias. Statistical measures exhibited a remarkable deterioration with increasing elevation-following a linear relationship; for example, percent bias was found to increase by a rate of 11.7% with every 400 m interval. A multiplicative bias correction scheme was devised, and Hydrological River Basin Environmental Assessment Model (Hydro-BEAM) was implemented to simulate flash floods driven by the uncorrected/corrected GSMaP data. Analysis of intensity thresholds revealed that appropriate threshold selection is critically important for the bias correction procedure. The hydrological model was calibrated for flash flood events during October-December 2007 and 2012 and validated during October-December, 2009 and 2010. Flash floods simulations were improved by the local bias correction procedure applied to the GSMaP data, but the degree of improvement varied from one period to another. The results of the study indicate that bias factors incorporating multiple variables such as extreme events and elevation variability have the potential to further improve flood simulations.

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Section: Satellite-based and Rain Gauge-based Precipitation Datasetsmentioning

confidence: 99%

Evaluation and Bias Correction of Satellite-Based Rainfall Estimates for Modelling Flash Floods over the Mediterranean region: Application to Karpuz River Basin, Turkey

Saber

Yılmaz

2018

Water

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“…The launch of precipitation radars in some satellite measurements, for example over the Tropical Rainfall Measuring Mission (TRMM) (Huffman et al, 2007) helps capture the three-dimensional structure of rain. In particular, the long-term TRMM on-board radar had obvious advantages for detecting the heavy precipitation that is associated with distinct orographic features and coastal effects (Shige et al, 2013(Shige et al, , 2015(Shige et al, , 2017. Other products such as CMORPH (Xie et al, 2017) and version 6 of the Global Precipitation Mission (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG) (Huffman et al, 2019(Huffman et al, , 2020 use a "morphing" based approach to estimate precipitation.…”

Section: Introductionmentioning

confidence: 99%

On the Robustness of Annual Daily Precipitation Maxima Estimates Over Monsoon Asia

Nguyen¹,

Bador²,

Alexander³

et al. 2020

Front. Clim.

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Understanding precipitation extremes over Monsoon Asia is vital for water resource management and hazard mitigation, but there are many gaps and uncertainties in observations in this region. To better understand observational uncertainties, this study uses a high-resolution validation dataset to assess the consistency of the representation of annual daily precipitation maxima (Rx1day) over land in 13 observational datasets from the Frequent Rainfall Observations on Grids (FROGS) database. The FROGS datasets are grouped into three categories: in situ-based and satellite-based with and without corrections to rain gauges. We also look at three sub-regions: Japan, India, and the Maritime Continent based on their different station density, orography, and coastal complexity. We find broad similarities in spatial and temporal distributions among in situ-based products over Monsoon Asia. Satellite products with correction to rain gauges show better general agreement and less inter-product spread than their uncorrected counterparts. However, this comparison also reveals strong sub-regional differences that can be explained by the quantity and quality of rain gauges. High consistency in spatial and temporal patterns are observed over Japan, which has a dense station network, while large inter-product spread is found over the Maritime Continent and India, which have sparser station density. We also highlight that while corrected satellite products show improvement compared to uncorrected products in regions of high station density (e.g., Japan) they have mixed success over other regions (e.g., India and the Maritime Continent). In addition, the length of record available at each station can also affect the satellite correction over these poorly sampled regions. Results of the additional comparison between all considered datasets and the sub-regional high resolution dataset remain the same, indicating that the overall quality of the station network has implications for the reliability of the in situ-based products derived and also the satellite products that use a correction to in situ data. Given these uncertainties in observations, there is no single best dataset for assessment of Rx1day in Monsoon Asia. In all cases we recommend users understand how each dataset is produced in order to select the most appropriate product to estimate precipitation extremes to fit their purpose.

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“…The negative biases can be as high as 20% of the ensemble means for WM and 36% for GPCC; however, the regions with such high rain rates are confined in orographically influenced regions such as along the windward coasts. This orographically associated issue has been discussed by several investigators [12,[31][32][33] and their results show that shallow orographic rain rates are underestimated in microwave radiometer algorithms due to weak ice scattering signatures [31] as well as the lack of gauge data for bias correction. As a result, new methods have been developed to correct the said issue [12,[31][32][33].…”

Section: Summary and Discussionmentioning

confidence: 98%

Evaluation of Precipitation Climatology Derived from TRMM Multi-Satellite Precipitation Analysis (TMPA) Monthly Product over Land with Two Gauge-Based Products

Liu

2015

Climate

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Abstract:The NASA/JAXA Tropical Rainfall Measuring Mission (TRMM) has been in operation for over 17 years since 1997. The length of TRMM is far shorter than those from ground observations, raising a question as to whether TRMM derived climatology products are good enough for research and applications. In this study, three climatologies derived from a blended product (the TRMM Multi-Satellite Precipitation Analysis (TMPA) monthly product or 3B43) and gauge-based ground observations (Global Precipitation Climatology Center (GPCC) and Willmott and Matsuura (WM)) are compared over land on a global scale (50° N-50° S) to assess the performance and weaknesses of the TMPA-derived climatology. Results show that the 3B43 climatology matches well with the two gauge-based climatologies in all seasons in terms of spatial distribution, zonal means as well as seasonal variations. However, large variations are found in light rain (<0.5 mm/day) regions such as the Sahara Desert. At high rain rates, large negative biases (3B43 < WM < GPCC) are found in orographically influenced regions such as windward sides of monsoon mountains in JJA, which is associated with underestimation of shallow orographic rain rates in microwave radiometer algorithms and sparse gauge data for bias correction. Nonetheless, biases in 3B43 exist and vary with geographic locations due to a number of factors such as gauge availability and algorithm issues.

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Improvement of TMI Rain Retrieval Over the Indian Subcontinent

Cited by 21 publications

References 67 publications

Evaluation and Bias Correction of Satellite-Based Rainfall Estimates for Modelling Flash Floods over the Mediterranean region: Application to Karpuz River Basin, Turkey

Evaluation and Bias Correction of Satellite-Based Rainfall Estimates for Modelling Flash Floods over the Mediterranean region: Application to Karpuz River Basin, Turkey

On the Robustness of Annual Daily Precipitation Maxima Estimates Over Monsoon Asia

Evaluation of Precipitation Climatology Derived from TRMM Multi-Satellite Precipitation Analysis (TMPA) Monthly Product over Land with Two Gauge-Based Products

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