Estimation of rainfall from infrared‐microwave satellite data for basin‐scale hydrologic modelling

Kalinga, Oscar Anthony; Gan, Thian Yew

doi:10.1002/hyp.7626

Cited by 22 publications

(13 citation statements)

References 39 publications

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“…The Stage IV dataset has been widely used as a basis to evaluate model simulations, satellite precipitation estimates, and radar precipitation estimates (Davis et al, 2006;Gourley et al, 2011;Kalinga and Gan, 2010;Lopez, 2011;Yuan et al, 2008). Here, we obtain the hourly Stage IV precipitation for 2004 --2017 from the NCAR/UCAR RDA (https://rda.ucar.edu/datasets/ds507.5/, last access: Dec 28, 2019).…”

Section: Three-dimensional Gridded Nexrad Radar (Gridrad) Datasetmentioning

confidence: 99%

A high-resolution unified observational data product of mesoscale convective systems and isolated deep convection in the United States for 2004–2017

Li¹,

Feng²,

Qian³

et al. 2020

Preprint

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Abstract. Deep convection possesses markedly distinct properties at different spatiotemporal scales. We present an original high-resolution (4 km, hourly) unified data product of mesoscale convective systems (MCSs) and isolated deep convection (IDC) in the United States east of the Rocky Mountains and examine their climatological characteristics from 2004 to 2017. The data product is produced by applying an updated FLEXTRKR (Flexible Object Tracker) algorithm to hourly satellite brightness temperature, radar reflectivity, and precipitation datasets. Analysis of the data product shows that MCSs are much larger and longer-lasting than IDC, but IDC occurs about 100 times more frequently than MCSs, with a mean convective intensity comparable to that of MCSs. Hence both MCS and IDC are essential contributors to precipitation east of the Rocky Mountains, although their precipitation shows significantly different spatiotemporal characteristics. IDC precipitation concentrates in summer in the Southeast with a peak in the late afternoon, while MCS precipitation is significant in all seasons, especially for spring and summer in the Great Plains. The spatial distribution of MCS precipitation amounts varies by seasons, while diurnally, MCS precipitation generally peaks during nighttime except in the Southeast. Potential uncertainties and limitations of the data product are also discussed. The data product is useful for investigating the atmospheric environments and physical processes associated with different types of convective systems, quantifying the impacts of convection on hydrology, atmospheric chemistry, and severe weather events, and evaluating and improving the representation of convective processes in weather and climate models. The data product is available at https://doi.org/10.25584/1632005 (Li et al., 2020).

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Section: Three-dimensional Gridded Nexrad Radar (Gridrad) Datasetmentioning

confidence: 99%

A high-resolution unified observational data product of mesoscale convective systems and isolated deep convection in the United States for 2004–2017

Li¹,

Feng²,

Qian³

et al. 2020

Preprint

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“…For instance, Hossain and Anagnostou [79] compared the TMI, SSM/I, and AMSR-E precipitation estimates, and tested the implementation of TMI, SSM/I, AMSR-E and TRMM IR precipitation estimates on streamflow prediction. Kalinga and Gan [80] developed an infrared-microwave rainfall algorithm (IMRA), which adjusts the QPE retrieved from the TRMM and GOES IR sensors with the TMI data, and applied it to the conceptual SACramento Soil Moisture Accounting (SAC-SMA) model. Since the launch of the TRMM, the TRMM rainfall products (e.g., TMPA) have been extensively implemented in streamflow/flood simulations [81][82][83][84][85][86][87][88].…”

Section: Implementation Of Satellite Precipitationmentioning

confidence: 99%

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

Grimaldi

Walker

et al. 2016

Remote Sensing

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Abstract:Fluvial flooding is one of the most catastrophic natural disasters threatening people's lives and possessions. Flood forecasting systems, which simulate runoff generation and propagation processes, provide information to support flood warning delivery and emergency response. The forecasting models need to be driven by input data and further constrained by historical and real-time observations using batch calibration and/or data assimilation techniques so as to produce relatively accurate and reliable flow forecasts. Traditionally, flood forecasting models are forced, calibrated and updated using in-situ measurements, e.g., gauged precipitation and discharge. The rapid development of hydrologic remote sensing offers a potential to provide additional/alternative forcing and constraint to facilitate timely and reliable forecasts. This has brought increasing interest to exploring the use of remote sensing data for flood forecasting. This paper reviews the recent advances on integration of remotely sensed precipitation and soil moisture with rainfall-runoff models for rainfall-driven flood forecasting. Scientific and operational challenges on the effective and optimal integration of remote sensing data into forecasting models are discussed.

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“…Many previous works have developed a number of MW methods to retrieve precipitation [13][14][15]. However, precipitation is not always associated with clouds and the VIS/IR methods cannot detect information below clouds [16]. Meanwhile, MW instruments are restricted to polar-orbiting platforms, meaning that they obtain only a small amount of data and have the disadvantages of low spatial and temporal resolutions [17].…”

Section: Introductionmentioning

confidence: 99%

Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013

et al. 2018

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Precipitation is a key aspect of the climate system. In this paper, the dependability of five satellite precipitation products (TRMM [Tropical Rainfall Measuring Mission] To completely evaluate these precipitation products, the annual, seasonal and monthly precipitation averages were calculated. Overall, the Huaihe River and Qinlin mountains are shown to have heavy precipitation to the southeast and lighter precipitation to the northwest. The comparison results indicate that Gauge correction (CMORPH_BLD) improves the quality of the original satellite products (CMORPH_RAW), resulting in the higher correlation coefficient (CC), the low relative bias (BIAS) and root mean square error (RMSE). Over China, the GSMaP_RENALYSIS outperforms other products and shows the highest CC (0.91) and lowest RMSE (0.85 mm/day) and all products except for PERSIANN_CDR exhibit underestimation. GSMaP_RENALYSIS gives the highest of probability of detection (81%), critical success index (63%) and lowest false alarm ratio (36%) while TRMM3BV42 gives the highest of frequency bias index (1.00). Over Tibetan Plateau, CMORPH_RAW demonstrates the poorest performance with the biggest BIAS (4.2 mm/month) and lowest CC (0.22) in December 2013. GSMaP_RENALYSIS displays quite consistent with in situ measurements in summer. However, GSMaP_RENALYSIS and CMORPH_RAW underestimate precipitation over South China. CMORPH_BLD and TRMM3BV42 show consistent with high CC (>0.8) but relatively large RMSE in summer.

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Estimation of rainfall from infrared‐microwave satellite data for basin‐scale hydrologic modelling

Cited by 22 publications

References 39 publications

A high-resolution unified observational data product of mesoscale convective systems and isolated deep convection in the United States for 2004–2017

A high-resolution unified observational data product of mesoscale convective systems and isolated deep convection in the United States for 2004–2017

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

Inter-Comparison and Evaluation of Remote Sensing Precipitation Products over China from 2005 to 2013

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