Evaluation of Radar Precipitation Estimates from the National Mosaic and Multisensor Quantitative Precipitation Estimation System and the WSR-88D Precipitation Processing System over the Conterminous United States

Wu, Wanru; Kitzmiller, David; Wu, Sicheng

doi:10.1175/jhm-d-11-064.1

Cited by 32 publications

(33 citation statements)

References 12 publications

(12 reference statements)

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“…In regards to SWE estimation, the reliability of NMQ has not been fully justified in the literature to the best of our knowledge. Our confidence in using the NMQ (or NEXRAD) data partially comes from its superior performance with liquid precipitation measurement, which has been validated by many studies using either surface observations or cross-validation with other remote sensing sensors [7,17,18]. The NMQ rainfall products have also been used as the benchmark to quantify the spaceborne rainfall measurements [5][6][7].…”

Section: Discussionmentioning

confidence: 95%

Snowfall Detectability of Nasa's Cloudsat: The First Cross-Investigation of Its 2c-Snow-Profile Product and National Multi-Sensor Mosaic Qpe (Nmq) Snowfall Data

Cao¹,

Hong²,

Chen³

et al. 2014

PIER

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Abstract-This study investigates snowfall detectability and snowfall rate estimation with NASA's CloudSat through the first evaluation of its newly released 2C-SNOW-PROFILE products using the National Mosaic and Multisensor QPE System (NMQ) snowfall products. The primary focus is on the detection and estimation of surface snowfall. The results show that the CloudSat product has good detectability of light snow (snow water equivalent less than 1 mm/h) but degrades in moderate and heavy snow (heavier than 1 mm/h). The analysis suggests that the new 2C-SNOW-PROFILE algorithm is insufficient in correcting signal losses due to attenuation. Its underestimation is well correlated to snowfall intensity. Issues of sensitivity and data sampling with ground radars, which may affect the interpretation of the results, are also discussed. This evaluation of the new 2C-SNOW-PROFILE algorithm provides guidance for applications of the product and identifies particular error sources that can be addressed in future versions of the CloudSat snowfall algorithm.

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Section: Discussionmentioning

confidence: 95%

Snowfall Detectability of Nasa's Cloudsat: The First Cross-Investigation of Its 2c-Snow-Profile Product and National Multi-Sensor Mosaic Qpe (Nmq) Snowfall Data

Cao¹,

Hong²,

Chen³

et al. 2014

PIER

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“…The CC (RMSE) of the radar QPE was higher (lower) than that of the CMORPH product, indicating that radar QPE may be superior for monitoring heavy rainfall. Using the independent precipitation observations, Wu et al [34] examined the performance of the MRMS product developed by NSSL at a 6-h and 1-km resolution over the CONUS (CONtinental United States). The CC was 0.855, and the RMSE was 2.1 mm/6 h, which indicates that the MRMS product has the same level of accuracy as the CMPA-1km product over China.…”

Section: Evaluations In Heavy Rainfall Eventsmentioning

confidence: 99%

China’s 1 km Merged Gauge, Radar and Satellite Experimental Precipitation Dataset

Shen¹,

Zhou

Yang³

et al. 2018

Remote Sensing

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Based on high-density gauge precipitation observations, high-resolution weather radar quantitative precipitation estimation (QPE) and seamless satellite-based precipitation estimates, a 1-km experimental gauge-radar-satellite merged precipitation dataset has been developed using the proposed local gauge correction (LGC) and optimal interpolation (OI) merging strategies. First, hourly precipitation analyses from approximately 40,000 automatic weather stations at 0.01 • resolution were used to correct bias in the radar QPE Group System (QPEGS), developed by the China Meteorological Administration (CMA) and the Climate Prediction Center Morphing (CMORPH) precipitation products. As precipitation events tend to have a more localized distribution at the hourly and 0.01 • resolutions, three core parameters were improved using the OI method. (a) The spatial dependence of the error variance for radar QPE was accounted for over six sub-regions in China and is shown as a non-linear function of the gauge precipitation analysis. (b) The spatial dependence of error correlation for the radar QPE decreased exponentially with distance. (c) The error of the hourly gauge-based precipitation analysis was quantified as a function of the precipitation amount and the gauge network density, using the Monte Carlo method to randomly sample the gauge observations over the dense gauge network. The performance of the 1-km experimental gauge-radar-satellite merged precipitation dataset (named as China Merged Precipitation Analysis: CMPA_1km) was assessed at 6 h-temporal resolutions and 0.03 • × 0.03 • spatial resolution using precipitation observations from 208 independent hydrological stations as a reference. Compared with radar QPE and CMORPH, the CMPA-1km showed obviously better accuracy in all sub-regions and during all seasons. In contrast, gauge analysis and CMPA-1km shared similar accuracy, but the latter could estimate heavy precipitation more accurately than the former, as well as the latter has the advantage of seamless spatial coverage. However, the CMPA-1km exhibits larger uncertainty during the cold season compared to the warm season, which will need further improvement in future work. The downscaled bias-corrected 0.01 • resolution CMORPH was employed to fill the gaps in regions, mainly in Western China and the Tibetan Plateau, where gauge and radar measurements are limited.

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“…This system is the result of collaborative research involving various U.S. research and government agencies and the Central Weather Bureau of Taiwan (Zhang et al 2011;Wu et al 2012;Zhang et al 2016). It is a multisensor system that includes data from 149 WSR-88D U.S. radars, 31 Canadian C-band weather radars, and the Rapid Update Cycle and Hydrometeorological Automated Data System gauge data for several multisensor QPE algorithms (Zhang et al 2011(Zhang et al , 2016.…”

Section: A Precipitation Datamentioning

confidence: 99%

“…1 In this work, radar precipitation from NMQ data also exploits their QC algorithms, which are not currently implemented in the NEXRAD Precipitation Processing System (Wu et al 2012). The NMQ radar precipitation mesh data in its native spatial resolution (0.018) are interpolated to 5 km.…”

Section: A Precipitation Datamentioning

confidence: 99%

An Improved QPE over Complex Terrain Employing Cloud-to-Ground Lightning Occurrences

Minjarez-Sosa

Castro

Cummins

et al. 2017

Journal of Applied Meteorology and Climatology

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A lightning-precipitation relationship (LPR) is studied at high temporal and spatial resolution (5 min and 5 km). As a proof of concept of these methods, precipitation data are retrieved from the National Severe Storms Laboratory (NSSL) NMQ product for southern Arizona and western Texas while lightning data are provided by the National Lightning Detection Network (NLDN). A spatial-and time-invariant (STI) linear model that considers spatial neighbors and time lags is proposed. A data denial analysis is performed over Midland, Texas (a region with good sensor coverage), with this STI model. The LPR is unchanged and essentially equal, regardless of the domain (denial or complete) used to obtain the STI model coefficients. It is argued that precipitation can be estimated over regions with poor sensor coverage (i.e., southern Arizona) by calibrating the LPR over well-covered domains that are experiencing similar storm conditions. To obtain a lightning-estimated precipitation that dynamically updates the model coefficients in time, a Kalman filter is applied to the STI model. The correlation between the observed and estimated precipitation is statistically significant for both models, but the Kalman filter provides a better precipitation estimation. The best demonstration of this application is a heavy-precipitation, high-frequency lightning event in southern Arizona over a region with poor radar and rain gauge coverage. By calibrating the Kalman filter over a data-covered domain, the lightning-estimated precipitation is considerably greater than that estimated by radar alone. Therefore, for regions where both rain gauge and radar data are compromised, lightning provides a viable alternative for improving QPE.

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Evaluation of Radar Precipitation Estimates from the National Mosaic and Multisensor Quantitative Precipitation Estimation System and the WSR-88D Precipitation Processing System over the Conterminous United States

Cited by 32 publications

References 12 publications

Snowfall Detectability of Nasa's Cloudsat: The First Cross-Investigation of Its 2c-Snow-Profile Product and National Multi-Sensor Mosaic Qpe (Nmq) Snowfall Data

Snowfall Detectability of Nasa's Cloudsat: The First Cross-Investigation of Its 2c-Snow-Profile Product and National Multi-Sensor Mosaic Qpe (Nmq) Snowfall Data

China’s 1 km Merged Gauge, Radar and Satellite Experimental Precipitation Dataset

An Improved QPE over Complex Terrain Employing Cloud-to-Ground Lightning Occurrences

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