Detection and Measurement of Snowfall from Space

Levizzani, Vincenzo; Laviola, Sante; Cattani, Elsa

doi:10.3390/rs3010145

Cited by 77 publications

(66 citation statements)

References 79 publications

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“…All five satellite datasets consistently exhibit very low correlation coefficients (Fig. 5a-e), due to the well-documented failure of retrieval techniques over snow-and ice-covered surfaces, and more generally their inability to detect snowfall in all conditions (e.g., Ferraro et al, 1998;Levizzani et al, 2011;Ebert et al, 2007;Yong et al, 2013;Peng et al, 2014). Conversely, the reanalysis datasets showed high correlation coefficients for most grid cells (Fig.…”

Section: Gauge-based Evaluation Of Gridded P Datasetsmentioning

confidence: 99%

MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data

Beck

Dijk

Levizzani

et al. 2017

Hydrol. Earth Syst. Sci.

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872

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Abstract. Current global precipitation (P ) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979-2015 with a 3-hourly temporal and 0.25 • spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite-and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0 % of the stations and a median R of 0.67 vs. 0.44-0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments (< 50 000 km 2 ) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) against daily Q observations with P from each of the different datasets. For the 1058 sparsely gauged catchments, representative of 83.9 % of the global land surface (excluding Antarctica), MSWEP obtained a median calibration NSE of 0.52 vs. 0.29-0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org.

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Section: Gauge-based Evaluation Of Gridded P Datasetsmentioning

confidence: 99%

MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data

Beck

Dijk

Levizzani

et al. 2017

Hydrol. Earth Syst. Sci.

Self Cite

872

640

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“…Global snowfall can only be monitored observationally using spaceborne instruments [3,4]. Spaceborne microwave sensors are particularly suitable for detecting and quantifying snowfall thanks to their unique ability to probe within clouds [5,6].…”

Section: Introductionmentioning

confidence: 99%

SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager

et al. 2018

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This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of the Cloud Profiling Radar (CPR) onboard CloudSat. It is composed of three modules for (i) snowfall detection, (ii) supercooled droplet detection and (iii) SWP retrieval. This algorithm takes into account environmental conditions to retrieve SWP and does not rely on any surface classification scheme. The snowfall detection module is able to detect 83% of snowfall events including light SWP (down to 1 × 10 −3 kg·m −2 ) with a false alarm ratio of 0.12. The supercooled detection module detects 97% of events, with a false alarm ratio of 0.05. The SWP estimates show a relative bias of −11%, a correlation of 0.84 and a root mean square error of 0.04 kg·m −2 . Several applications of the algorithm are highlighted: Three case studies of snowfall events are investigated, and a 2-year high resolution 70 • S-70 • N snowfall occurrence distribution is presented. These results illustrate the high potential of this algorithm for snowfall detection and SWP retrieval using GMI.

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“…Therefore, accurate detection and estimation of snowfall is much desirable in meteorological, hydrological, and climatological applications [1,2]. Nowadays, remote sensing technology has evolved as a practical option for monitoring snowfall, especially across regional and global scales [1,3].…”

Section: Introductionmentioning

confidence: 99%

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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Detection and Measurement of Snowfall from Space

Cited by 77 publications

References 79 publications

MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data

MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data

SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager

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

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