Assessment of the Advanced Very High-Resolution Radiometer (AVHRR) for Snowfall Retrieval in High Latitudes Using CloudSat and Machine Learning

Ehsani, Mohammad Reza; Behrangi, Ali; Adhikari, Abishek; Song, Yang; Huffman, George J.; Adler, Robert F.; Bolvin, David T.; Nelkin, Eric

doi:10.1175/jhm-d-20-0240.1

Cited by 23 publications

(11 citation statements)

References 78 publications

(75 reference statements)

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“…Along these lines, we could exhaustively test a completely different model such as random forest regression. Random forest regression is a powerful method that has been used for atmospheric science applications in the past [81]. We did some preliminary testing with a random forest regression model, and it worked almost as well as the MLR and ANN models (See Appendix E for preliminary results).…”

Section: Discussionmentioning

confidence: 99%

Data Filling of Micrometeorological Variables in Complex Terrain for High-Resolution Nowcasting

et al. 2022

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In this paper, two different computationally inexpensive methods for nowcasting/data filling spatially varying meteorological variables (wind velocity components, specific humidity, and virtual potential temperature) covering scales ranging from 100 m to 5 km in regions marked by complex terrain are compared. Multivariable linear regression and artificial neural networks are used to predict micrometeorological variables at eight locations using the measurements from three nearby weather stations. The models are trained using data gathered from a system of eleven low-cost automated weather stations that were deployed in the Cadarache Valley of southeastern France from December 2016 to June 2017. The models are tested on two held-out periods of measurements of thermally-driven flow and synoptically forced flow. It is found that the models have statistically significant performance differences for the wind components during the synoptically driven flow period (p = 6.6 × 10−3 and p = 2.0 × 10−2 for U and V, respectively), but perform the same otherwise. These methods can be used to spatially fill gaps in micrometeorological datasets. Recommended future work should include statistically interpreting the predictive models and testing their capabilities on meteorological datasets from different locations.

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Section: Discussionmentioning

confidence: 99%

Data Filling of Micrometeorological Variables in Complex Terrain for High-Resolution Nowcasting

et al. 2022

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“…Finally, the CloudSat precipitation datasets are increasingly used as sources of training and a priori constraints for other retrievals. Recent machine learning applications use collections of CloudSat profiles coincident with the desired remote sensing observations as training datasets or constraints [24][25][26]. For coincident observations themselves, the DO-Op sampling issues that impact global, regional, and zonal precipitation properties as described in Section 4 do not come into play; however, a collection of such coincident observations from the DO-Op period will be biased toward day-time observations.…”

Section: Discussionmentioning

confidence: 99%

Biases in CloudSat Falling Snow Estimates Resulting from Daylight-Only Operations

Milani

Wood

2021

Remote Sensing

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Falling snow is a key component of the Earth’s water cycle, and space-based observations provide the best current capability to evaluate it globally. The Cloud Profiling Radar (CPR) on board CloudSat is sensitive to snowfall, and other satellite missions and climatological models have used snowfall properties measured by it for evaluating and comparing against their snowfall products. Since a battery anomaly in 2011, the CPR has operated in a Daylight-Only Operations (DO-Op) mode, in which it makes measurements primarily during only the daylit portion of its orbit. This work provides estimates of biases inherent in global snowfall amounts derived from CPR measurements due to this shift to DO-Op mode. We use CloudSat’s snowfall measurements during its Full Operations (Full-Op) period prior to the battery anomaly to evaluate the impact of the DO-Op mode sampling. For multi-year global mean values, the snowfall fraction during DO-Op changes by −10.16% and the mean snowfall rate changes by −8.21% compared with Full-Op. These changes are driven by the changes in sampling in DO-Op and are very little influenced by changes in meteorology between the Full-Op and DO-Op periods. The results highlight the need to sample consistently with the CloudSat observations or to adjust snowfall estimates derived from CloudSat when using DO-Op data to evaluate other precipitation products.

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“…The depression in the brightness temperatures due to the ice particles is linked to the precipitation rate. Because of the high‐frequency channels, MHS has shown skill in retrieving snowfall (Adhikari et al., 2020; Bennartz & Bauer, 2003; Ehsani et al., 2021; Noh & Liu, 2004). MHS is available on several NOAA and MetOp satellites.…”

Section: Datamentioning

confidence: 99%

Assessment of Satellite Precipitation Products in Relation With Orographic Enhancement Over the Western United States

Adhikari

Behrangi

2022

Earth and Space Science

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Satellite-based precipitation estimates have been widely used in many research and application areas, including short-term weather and long-term climate prediction (Arkin & Ardanuy, 1989;Huffman et al., 1995). Precipitation estimates from satellite measurements are unique due to their global coverage, including oceanic and high terrain mountainous regions where ground-based observations are not always possible (Kidd et al., 2017). It is difficult to get broad spatial coverage in mountainous areas using rain gauges, and ground-based radar observation has limitations (Sapiano & Arkin, 2009). Despite the great coverage of satellite products, their precipitation retrieval accuracy in mountain regions is still a challenge (Mei et al., 2014;Shige et al., 2013). The primary types of sensors used to estimate precipitation from satellite measurements are radar, passive microwave imager/ sounder, and Infrared radiometers. The space-borne radar onboard satellites such as the Tropical Rainfall Measuring Mission (TRMM; Kummerow et al., 1998) and the Global Precipitation Measurement (GPM; Hou et al., 2014;Skofronick-Jackson et al., 2018) have shown success in measuring precipitations globally, but they still suffer from uncertainties especially in mountainous regions (Adhikari et al., 2019). One major source of uncertainty associated with space-borne radars is the contamination of near-surface reflectivity profiles due to ground-clutter (Arulraj & Barros, 2019;Liao et al., 2014). Passive microwave sensors (PMW) are popular and have been widely used in estimating precipitation for decades (Prigent, 2010;Wilheit, 1986). The benefit of using PMW sensors compared to radar is their higher sampling rate, although they can misclassify rainfall over mountainous regions (Yamamoto & Shige, 2015). For example, snow and ice-covered surfaces over the mountains could be classified wrongly as precipitating clouds resulting in an overestimation of precipitation. The PMW precipitation estimates mainly rely on the cumulative signal from the vertical column of cloud, hydrometeors and their emission or scattering properties. The heavy rainfall associated with mountainous regions can be generated from clouds with no or little ice aloft (You & Liu, 2012), resulting in an underestimation in PMW estimates (Dinku et al., 2010;Houze, 2012;Shige et al., 2013). Studies have shown that satellite-based precipitation products underestimate precipitation rate over several mountainous regions of the globe such as Japan (Kubota et al., 2009;Shige et al., 2013), Africa

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Assessment of the Advanced Very High-Resolution Radiometer (AVHRR) for Snowfall Retrieval in High Latitudes Using CloudSat and Machine Learning

Cited by 23 publications

References 78 publications

Data Filling of Micrometeorological Variables in Complex Terrain for High-Resolution Nowcasting

Data Filling of Micrometeorological Variables in Complex Terrain for High-Resolution Nowcasting

Biases in CloudSat Falling Snow Estimates Resulting from Daylight-Only Operations

Assessment of Satellite Precipitation Products in Relation With Orographic Enhancement Over the Western United States

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