A new approach for bias adjustment of IMERG remotely sensed snowfall product
- PDF / 1,299,425 Bytes
- 16 Pages / 595.276 x 790.866 pts Page_size
- 94 Downloads / 170 Views
ORIGINAL PAPER
A new approach for bias adjustment of IMERG remotely sensed snowfall product Leili Sadeghi 1 & Bahram Saghafian 1
&
Saber Moazami 2
Received: 25 June 2020 / Accepted: 2 October 2020 # Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract Due to uncertainty in the nature of indirect remotely sensed precipitation products, bias adjustment is a crucial step. In this research, bias adjustment of Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement) (IMERG) snowfall estimates with 0.1° spatial resolution and half-hour temporal resolution was performed over a basin located in the Western United States based on Snowpack Telemetry (SNOTEL) ground station snow accumulation (SA) observations using the Gridded based Bias Adjustment using Copula (GBAC) approach. After dividing the study area into several boxes, usual and unusual pairs of cumulative distribution functions (CDFs) were separated in station pixels. Then, simulated biases were obtained using multivariate copula functions in station boxes. Next, based on the T copula function, bias adjustment bands were generated during the calibration period in station pixels. This step was followed by the validation stage. Also, bias adjustment bands were generated in pixels with no station. Based on calibration results, in boxes located west of 120° W, the range between the lower bound and the 10th percentile of the band had the most impact on satellite SA adjustment. Conversely, in boxes located east of 120° W, the middle range of the band (between 10th and 90th percentiles) was more effective. Thus, longitude was an effective factor in satellite SA bias adjustment. The validation results showed that in station boxes with a longitude over 120° west, the adjustment band was narrower. This is while in station boxes with longitude less than 120° west, the bias adjustment band better encompassed the ground SA data.
1 Introduction In recent years, increased use of remote sensing technology in water-related problems is reported. Satellite products provide precipitation measurements with acceptable spatial and temporal resolutions. But due to uncertainty sources in indirect precipitation measurement, bias affects remote sensing estimates and results in overestimation/underestimation of precipitation. Therefore, one must identify and adjust bias in remote sensing estimates in order to produce reliable information for practical uses. On the other hand, snow is one of the major forms of precipitation in cold and mountainous regions. On average, 60% of the northern hemisphere is snow-covered in * Bahram Saghafian [email protected] 1
Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2
Department of Civil Engineering, Environmental Sciences Research Centre, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran
mid-winter while more than 30% of the Earth’s surface receives seasonal snowfall and about 10% of the Earth’s surface is permanently snow-covered (Dozier 1989)
Data Loading...
