Next Article in Journal
Recursive Local Summation of RX Detection for Hyperspectral Image Using Sliding Windows
Next Article in Special Issue
Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables
Previous Article in Journal
Bayesian Cloud Detection for 37 Years of Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC) Data
Previous Article in Special Issue
Can Satellite Precipitation Products Estimate Probable Maximum Precipitation: A Comparative Investigation with Gauge Data in the Dadu River Basin
Article

Incorporating Satellite Precipitation Estimates into a Radar-Gauge Multi-Sensor Precipitation Estimation Algorithm

1
Office of Water Prediction (OWP), National Weather Service (NWS), NOAA, Silver Spring, MD 20910, USA
2
University Corporation for Atmospheric Research (UCAR), Boulder, CO 80307, USA
3
University of Texas at Arlington, Arlington, Texas 76019, USA
4
NOAA/NESDIS/Center for Satellite Applications and Research, College Park, MD 20740, USA
5
Physical Sciences Division, NOAA/Earth System Research Laboratory, Boulder, CO 80305, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(1), 106; https://doi.org/10.3390/rs10010106
Received: 25 September 2017 / Revised: 21 November 2017 / Accepted: 10 January 2018 / Published: 13 January 2018
This paper presents a new and enhanced fusion module for the Multi-Sensor Precipitation Estimator (MPE) that would objectively blend real-time satellite quantitative precipitation estimates (SQPE) with radar and gauge estimates. This module consists of a preprocessor that mitigates systematic bias in SQPE, and a two-way blending routine that statistically fuses adjusted SQPE with radar estimates. The preprocessor not only corrects systematic bias in SQPE, but also improves the spatial distribution of precipitation based on SQPE and makes it closely resemble that of radar-based observations. It uses a more sophisticated radar-satellite merging technique to blend preprocessed datasets, and provides a better overall QPE product. The performance of the new satellite-radar-gauge blending module is assessed using independent rain gauge data over a five-year period between 2003–2007, and the assessment evaluates the accuracy of newly developed satellite-radar-gauge (SRG) blended products versus that of radar-gauge products (which represents MPE algorithm currently used in the NWS (National Weather Service) operations) over two regions: (I) Inside radar effective coverage and (II) immediately outside radar coverage. The outcomes of the evaluation indicate (a) ingest of SQPE over areas within effective radar coverage improve the quality of QPE by mitigating the errors in radar estimates in region I; and (b) blending of radar, gauge, and satellite estimates over region II leads to reduction of errors relative to bias-corrected SQPE. In addition, the new module alleviates the discontinuities along the boundaries of radar effective coverage otherwise seen when SQPE is used directly to fill the areas outside of effective radar coverage. View Full-Text
Keywords: multi-sensor fusion; satellite; radar; precipitation multi-sensor fusion; satellite; radar; precipitation
Show Figures

Graphical abstract

MDPI and ACS Style

He, Y.; Zhang, Y.; Kuligowski, R.; Cifelli, R.; Kitzmiller, D. Incorporating Satellite Precipitation Estimates into a Radar-Gauge Multi-Sensor Precipitation Estimation Algorithm. Remote Sens. 2018, 10, 106. https://doi.org/10.3390/rs10010106

AMA Style

He Y, Zhang Y, Kuligowski R, Cifelli R, Kitzmiller D. Incorporating Satellite Precipitation Estimates into a Radar-Gauge Multi-Sensor Precipitation Estimation Algorithm. Remote Sensing. 2018; 10(1):106. https://doi.org/10.3390/rs10010106

Chicago/Turabian Style

He, Yuxiang; Zhang, Yu; Kuligowski, Robert; Cifelli, Robert; Kitzmiller, David. 2018. "Incorporating Satellite Precipitation Estimates into a Radar-Gauge Multi-Sensor Precipitation Estimation Algorithm" Remote Sens. 10, no. 1: 106. https://doi.org/10.3390/rs10010106

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop