The assessment of Global Precipitation Measurement estimates over the Indian subcontinent

Krishna, U. V. Murali; Das, Subrata Kumar; Deshpande, Sachin M.; Doiphode, S. L.; Pandithurai, G.

doi:10.1002/2017ea000285

Cited by 44 publications

(26 citation statements)

References 60 publications

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“…The one-minute DSD measurements obtained from JWD are fitted with a three-parameter gamma distribution, as suggested by Ulbrich (1983). The details about the DSDs used in the present study can be found in Das et al (2017) and Krishna et al (2017).…”

Section: Instrument and Datasetsmentioning

confidence: 99%

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Statistical characteristics of raindrop size distribution over Western Ghats of India: wet versus dry spells of Indian Summer Monsoon

Krishna¹,

Das²,

Sulochana³

et al. 2020

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Abstract. The nature of raindrop size distribution (DSD) is analyzed during wet and dry spells of the Indian Summer Monsoon (ISM) in the Western Ghats (WGs) region by using Joss-Waldvogel Disdrometer (JWD) measurements. The observed DSDs are fitted with gamma distribution, and the DSD characteristics are studied during ISM season (June–September) of 2012–2015. The DSD spectra show distinct diurnal variation during the wet and dry spells. The dry spells exhibit a strong diurnal cycle with two peaks, while the diurnal cycle is not so prominent in the wet spells. Results reveal the microphysical characteristics of warm rain during both the wet and dry periods. Even though the warm rain processes are dominant in the WGs region, the underlying dynamical processes cause the differences in DSD characteristics during the wet and dry spells. In addition, the differences in DSD spectra with different rain rates are also observed. The DSD spectra are further analyzed by separating into stratiform and convective types. Finally, an empirical relationship between the slope parameter, Λ and shape parameter, µ is derived by best fitting the quadratic polynomial for the observed data during both wet and dry spells as well as for the stratiform and convective types of rain. The µ-Λ relations obtained in the present study are slightly different in comparison with the previous studies.

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Section: Instrument and Datasetsmentioning

confidence: 99%

“…13.6 GHz and Ka-band at ~35.5 GHz. The details of the satellite mission can be found in Huffman et al (2015), and the dataset used in the present analysis can be found in Krishna et al (2017).…”

Section: Dsd Overview-topographic Perspectivementioning

confidence: 99%

Statistical characteristics of raindrop size distribution over Western Ghats of India: wet versus dry spells of Indian Summer Monsoon

Krishna¹,

Das²,

Sulochana³

et al. 2020

Preprint

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“…Murali Krishna et al (2017) showed that the GPM measurements improved over the Western Ghats region compared to TRMM measurements. They further assessed the DSD measurements from GPM https://doi.org/10.5194/acp-2019-832 Preprint.…”

Section: Effect Of Orography On Raindrop Size Distribution Over Wgsmentioning

confidence: 99%

mentioning

confidence: 99%

Statistical characteristics of raindrop size distribution over Western Ghats of India: wet versus dry spells of Indian Summer Monsoon

Krishna¹,

Das²,

Sulochana³

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. The nature of raindrop size distribution (DSD) is analyzed during wet and dry spells of the Indian Summer Monsoon (ISM) over Western Ghats (WGs) using Joss-Waldvogel Disdrometer (JWD) measurements. The observed DSDs are fitted with gamma distribution, and the characteristic DSDs are studied during the summer monsoon seasons (June&#8211;September) of 2012&#8211;2015. The DSD spectra show distinct diurnal variation during wet and dry spells. The dry spells exhibit a strong diurnal cycle with two peaks, while the diurnal cycle is not prominent in the wet spells. The observational results reveal the microphysical characteristics of warm rain during both the wet and dry spells. Even though the warm rain processes are dominant over WGs during monsoon, the underlying dynamical processes cause the differences in DSD characteristics during wet and dry spells. In addition, the differences in DSD spectra with different rain rates are also observed during the wet and dry spells. The DSD spectra are further analyzed by separating into stratiform and convective types. Finally, an empirical relation between slope parameter, &#923; and shape parameter, &#956; is derived by best fitting the quadratic polynomial for the observed data during both wet and dry spells as well as for the stratiform and convective types of precipitation. The &#923;&#8211;&#956; relations obtained in the present study are slightly different in comparison with the earlier studies.

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“…Moreover, previous studies have assessed the performance of IMERG over varied topographic and geographic features considering seasonal characteristics [19,[21][22][23][24][25][26][27][28][29]. The validation of the IMERG dataset using ground-based observations (i.e., gauges and radar) in conterminous US and Canada suggests an overall improvement in surface precipitation measurement from previous similar products [22,26].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Error Modeling to Improve GPM IMERG Precipitation Product over the Brahmaputra River Basin

et al. 2020

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The Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) Level 3 estimates rainfall from passive microwave sensors onboard satellites that are associated with several uncertainty sources such as sensor calibration, retrieval errors, and orographic effects. This study aims to provide a comprehensive investigation of multiple machine learning (ML) techniques (Random Forest, and Neural Networks), to stochastically generate an error-corrected improved IMERG precipitation product at a daily time scale and 0.1°-degree spatial resolution over the Brahmaputra river basin. In this study, we used the operational IMERG-Late Run version 06 product along with several meteorological and land surface parameters (elevation, soil type, land type, soil moisture, and daily maximum and minimum temperature) to produce an improved precipitation product in the Brahmaputra basin. We trained, tested, and optimized ML algorithms using 4 years (from 2015 through 2019) of reference rainfall data derived from the rain gauge. The ML generated precipitation product exhibited improved systematic and random error statistics for the study area, which is a strong indication for using the proposed algorithms in retrieving precipitation across the globe. We conclude that the proposed ML-based ensemble framework has the potential to quantify and correct the error sources for improving and promoting the use of satellite-based precipitation estimates for water resources applications.

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The assessment of Global Precipitation Measurement estimates over the Indian subcontinent

Cited by 44 publications

References 60 publications

Statistical characteristics of raindrop size distribution over Western Ghats of India: wet versus dry spells of Indian Summer Monsoon

Statistical characteristics of raindrop size distribution over Western Ghats of India: wet versus dry spells of Indian Summer Monsoon

Statistical characteristics of raindrop size distribution over Western Ghats of India: wet versus dry spells of Indian Summer Monsoon

Machine Learning-Based Error Modeling to Improve GPM IMERG Precipitation Product over the Brahmaputra River Basin

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